Reading and Discussions: The Best Way to Learn English?

Reading and Discussions: The Best Way to Learn English?

By Janpha Thadphoothon

This blog post is part of my reflection on my professional practice as an English teacher in Bangkok, Thailand. In my opinion, one of the best ways to master the English language (or any other language) is through a combination of reading and discussing what one has read. I am sure you would agree with me that language learning is not just about memorizing vocabulary or grammar rules; it is about immersing oneself in the language in meaningful and engaging ways.

I am not an expert in teaching English, but I have been in the field for over 30 years. As someone who has worked with learners from diverse backgrounds, I think I can offer some insights from my long-time experience. You see, teaching and learning English can be both challenging and rewarding, but the methods we adopt make all the difference.

Why Reading?

People often say that reading is the gateway to knowledge. When it comes to learning English, reading serves as a foundation. Whether it’s news reports, articles, book chapters, blogs, or even websites, reading provides exposure to vocabulary, grammar structures, and cultural nuances. It is a way to develop not only your reading skills but also your overall understanding of the language.

For example, let’s take news reports. These are excellent tools because they often use formal language, provide context, and introduce learners to current events. Articles and blogs, on the other hand, can be more conversational and engaging. Each type of text contributes to different aspects of language acquisition.

What we have learned so far is that reading extensively helps build a solid base. However, reading alone is not enough. The act of discussion amplifies the learning process, turning passive intake into active use.

I feel that students may be reading less extensively despite the abundance of texts and materials available. Online reading has not been particularly successful in fostering a genuine reading habit or appreciation among younger generations. Of course, I could be wrong.

After reading something, the next natural step is to discuss it with others—or even with AI agents like ChatGPT. Engaging in discussions about the topics related to your reading helps deepen your understanding and increases your exposure to the language.

 Why Discussions?

After reading a text, discussing it allows learners to practice their speaking skills. It also ensures that they have understood the material. I guess it is fair to say that discussions create an interactive learning environment where ideas flow, and learners feel encouraged to participate.

Think about a round-robin brainstorming session, for instance. This technique ensures that everyone in a group gets an equal chance to share their thoughts. It’s not just about speaking; it’s about listening, rephrasing, and responding—all critical skills in mastering English.

Let me illustrate this with a simple question: If you could have your own business one day, what would it be? Now imagine discussing this with your peers. A café bookstore? A spa shop? Each idea sparks new vocabulary, questions, and opportunities to learn.

Practical Steps for Teachers and Learners

For those teaching or learning English, here’s a practical framework:

1. Choose Engaging Reading Materials: Select news articles, short stories, or blogs that match the learners' level and interests.
2. Comprehension First: Before discussing, ensure the text is well understood. This can be achieved through comprehension questions or summaries.
3. Encourage Paraphrasing: Restating or rephrasing parts of the text helps learners internalize the language.
4. Foster Open Discussions: Create a safe space for everyone to contribute. Techniques like round-robin ensure equal participation.

A Personal Take

I think reading and discussions are timeless methods in language education. They promote critical thinking, cultural awareness, and, most importantly, confidence. In my experience, students who regularly engage in these activities not only improve their English but also their ability to express themselves.

To conclude, the journey to mastering English is a continuous one, but methods like reading and discussions can make it enriching and enjoyable. What are your thoughts? Have you tried these methods in your learning or teaching journey?

Janpha Thadphoothon is an assistant professor of ELT at the International College, Dhurakij Pundit University in Bangkok, Thailand. Janpha Thadphoothon also holds a certificate of Generative AI with Large Language Models issued by DeepLearning.AI.

 

Business Proverbs and Words of Wisdom

By Janpha Thadphoothon

The business world is brimming with wisdom embedded in proverbs and expressions. These nuggets of wisdom have been passed down through generations, offering practical advice and insights into how businesses operate and succeed. I am sure that you would agree with me that these sayings are not only enlightening but also highly applicable to our everyday lives and professional endeavors.

In this post, I’ll share some of my favorite business proverbs and explore their meanings. These proverbs are not only valuable for business professionals but also for language learners, as they enhance vocabulary and cultural understanding.

The Value of Business Proverbs

It is my idea that proverbs act as mirrors of collective human experience. In the context of business, they reflect hard-earned lessons from the successes and failures of entrepreneurs, employees, and leaders. People say that proverbs are a kind of distilled wisdom—short, memorable, and packed with meaning.

Take, for example, the saying “Money does not grow on trees.” This reminds us that wealth must be earned through effort and careful management. It’s easy to overspend or waste resources, but this saying teaches us to value money and use it wisely.

I think this kind of wisdom is universal, transcending cultures. Whether you're in Thailand, the United States, or anywhere else, you'll find similar proverbs that caution against wastefulness and emphasize hard work.

A Few Timeless Business Proverbs

Here are some of my favorite business-related sayings:

1. “Time is money.”
   This is one of the most famous proverbs in business. It emphasizes the importance of time and reminds us that every wasted moment is a lost opportunity to create value.

2. “Don’t put all your eggs in one basket.”
   This proverb stresses the importance of diversification, whether it’s in investment, product development, or partnerships. If you concentrate all your resources in one place, you risk losing everything if that venture fails.

3. “The early bird catches the worm.”
   People say that being proactive and seizing opportunities early gives you a competitive advantage. This is particularly true in today’s fast-paced business environment.

4. “A fool and his money are soon parted.”
   This warns against reckless spending and poor financial decisions. It serves as a reminder to think carefully about where and how we invest our money.

5. “Rome wasn’t built in a day.”
   Success takes time and consistent effort. This is a valuable lesson for startups and small businesses that may feel discouraged by slow progress.

Applying Proverbs to Business Communication

Proverbs are not just abstract ideas; they can be applied in practical ways, especially in communication. For example, when discussing a new project, you might say, “Let’s not put all our eggs in one basket” to suggest spreading risks.

Here’s how you can use proverbs in conversation:

• To agree: “I agree with you 100 percent. That’s so true!”
• To disagree: “I don’t think so. I beg to differ.”

In my experience, proverbs can make your speech more persuasive and relatable. They show that you’re drawing on shared wisdom, which can build trust and credibility in business discussions.

Learning Business Proverbs as an EFL Student

For students learning English as a foreign language, business proverbs offer a fun and engaging way to develop language skills. It is obvious that proverbs improve vocabulary, comprehension, and cultural knowledge.

Here’s an activity I often recommend to my students:

1. Spend five minutes finding a business proverb you like.
2. Share it with your classmates and explain why you chose it.
3. Discuss whether you agree or disagree with its message.

This kind of exercise not only improves language skills but also fosters critical thinking and discussion.

Proverbs and the Digital Age

You would agree with me that some business wisdom remains timeless, even in the age of technology. For example, “Time is money” has never been more relevant than it is today, when digital tools allow us to automate tasks and save time.

However, the digital age has also given rise to new proverbs, such as:

• “Data is the new oil.”
• “Your network is your net worth.”

These modern sayings reflect the evolving priorities of businesses, emphasizing the value of data and relationships in the digital economy.

Your Turn: Share Your Wisdom

I think everyone has a favorite business proverb or two. What’s yours? Here’s an idea: let’s take this discussion online! Share your favorite sayings in the comments below and explain what they mean to you.

For example, do you agree with the idea that “You have to spend money to make money”? Or do you prefer a more cautious approach, like “A penny saved is a penny earned”?

So,..

Business proverbs and words of wisdom are powerful tools for understanding and navigating the complexities of the professional world. They encapsulate valuable lessons in a concise and memorable way.

In my opinion, proverbs are more than just sayings—they are guiding principles that can inspire us, teach us, and help us make better decisions. So the next time you hear someone say, “Don’t bite off more than you can chew,” take a moment to reflect on its meaning and how it applies to your life or work.

Let’s keep learning from these timeless words of wisdom. After all, as another proverb goes, “Knowledge is power.” and "Practice makes perfect."

Janpha Thadphoothon is an assistant professor of ELT at the International College, Dhurakij Pundit University in Bangkok, Thailand. Janpha Thadphoothon also holds a certificate of Generative AI with Large Language Models issued by DeepLearning.AI.

In Search of Silence and Solitude

 In Search of Silence and Solitude

By Janpha Thadphoothon

Amidst the noises and confusion (online), the last thing we need is more data and noises. It is priviledged  to have opportunities to find peace and solitude, especially silence and solitude in nature.

We all need to connect with nature. The reality of life is that we need to find and enjoy nature.

Promoting Healthy Silence and Solitude Amid Digital Overload [DRAFT ONLY]

 

Promoting Healthy Silence and Solitude Amid Digital Overload [DRAFT ONLY]

Janpha Thadphoothon and Yongyuth Khamkhong

In today’s hyperconnected world, Thailand is not immune to the challenges of digital overload. Excessive use of social media and electronic devices among children, teens, and even adults has raised alarms across the nation. Stories of toddlers glued to screens and teenagers struggling with digital addiction are increasingly common. This concern has not gone unnoticed, yet many feel the Thai authorities have been slow to act decisively.

The Impact of Digital Overload

Yuval Noah Harari told us that we have had far too much information.

Digital devices, while essential in many ways, have disrupted traditional human interactions and compromised our ability to focus. The younger generation is particularly vulnerable, with many children as young as three or four years old exposed to hours of screen time daily. This not only affects their mental health but also hinders their social and cognitive development.

Adults, too, are not spared. Many find themselves trapped in endless social media scrolling or working long hours online, leaving little room for reflection, real-world connections, or mindfulness.

The Need for Silence and Solitude

Silence and solitude are essential for mental clarity, emotional well-being, and focus. They allow individuals to recharge, process their thoughts, and develop creativity. Yet, in the age of constant notifications, achieving such moments has become increasingly difficult.

What Thailand Can Learn from Australia

Recently, Australia made headlines by approving the world’s strictest laws to regulate children's access to social media. The new law will ban children under 16 from using social media platforms without parental consent. Companies that fail to comply could face fines of up to A$50m ($32.5m; £25.7m). Although the law will not take effect for at least 12 months, it sends a powerful message about prioritizing children's mental health and well-being.

Thailand could draw inspiration from this bold move by enacting stricter regulations on screen time for children. Such policies could serve as a foundation for broader initiatives to combat digital addiction and promote healthier habits.

Emerging Trends in Thailand

Although Thailand’s response to digital overload has been slower, some positive trends are emerging:

1. Digital Detox Campaigns

Several non-governmental organizations (NGOs) and schools have initiated digital detox campaigns. These programs encourage families to designate "screen-free" hours, particularly during meals or bedtime, to foster better relationships and communication.

2. Mindfulness and Meditation Practices in Schools

Mindfulness and meditation, rooted in Thai Buddhist traditions, are making a comeback in schools. These practices help children and teens develop focus and emotional regulation. For example, some schools now start the day with 10 minutes of guided meditation, offering students a moment of calm before engaging in their studies.

3. Nature-Based Activities

There has been a renewed push to reconnect with nature. Programs that encourage outdoor activities, such as hiking, gardening, or weekend family trips to national parks, are gaining popularity. Spending time in nature not only reduces screen dependency but also promotes mindfulness and well-being.

4. Community Engagement Projects

Community-driven activities, such as local sports events, art workshops, and cultural festivals, provide opportunities for individuals to engage in meaningful face-to-face interactions. These events are increasingly being designed to encourage participants to leave their devices behind.

5. Government and Health Authority Interventions

While the Thai Ministry of Public Health has begun rolling out awareness campaigns about the dangers of excessive screen time, their efforts lack the boldness of Australia’s new law. Thailand could benefit from implementing similar strict measures to regulate children's social media use.

Recommendations for Moving Forward

To address digital overload effectively, Thailand should adopt a multi-pronged approach:

• Parental Guidance: Parents play a crucial role in setting boundaries for screen time and modeling balanced digital habits.
• Policy Implementation: The government should consider stricter guidelines, similar to Australia’s, to regulate children’s access to social media.
• Promoting Human Interaction: Schools and workplaces should prioritize activities that foster human connections, such as team-building exercises and collaborative projects.
• Personal Accountability: Each individual must recognize the importance of setting aside time for reflection, self-care, and meaningful offline experiences.

Final Thoughts

The digital age has brought immense benefits, but it has also created challenges that require urgent attention. Thailand’s emerging efforts to promote silence and solitude are promising, but there is still much to be done. By looking to Australia’s decisive actions as a model and strengthening local initiatives, Thailand can create an environment where silence, mindfulness, and meaningful connections thrive.

Let us all take a moment to pause, reflect, and reconnect—not just with our devices, but with ourselves and the world around us.

L2 Voice in Writing: A Must in the Digital Age

L2 Voice in Writing: A Must in the Digital Age

By Janpha Thadphoothon

In today's digital landscape, having a distinct and personal voice in writing is no longer optional—it’s a must. This applies not only to non-native English writers but also to native speakers navigating the vast ocean of content created by both humans and AI. The ability to express oneself authentically and uniquely has become a valuable skill in an era dominated by generative AI tools capable of producing vast amounts of text in a "generic" voice.

In the field of applied linguistics, this concept is often referred to as L2 Voice in Writing. It explores how non-native speakers express themselves in a second language (L2), highlighting how writers represent their identities through their words. This self-representation is achieved through a combination of discursive features (like word choice and sentence structure) and non-discursive elements (like tone and style).

Unique Voice versus Generic Voice

One of the dangers in the digital age is the temptation to let your voice fade into obscurity. This is particularly relevant when using AI tools, which excel at producing polished but generic text. While AI-generated content may be grammatically accurate and contextually relevant, it often lacks the personal touch and originality that come from authentic self-expression.

To prevent this, writers must be conscious of their unique voice—whether they are native or non-native English speakers. Your voice is what makes your writing memorable, persuasive, and truly yours. It reflects your personality, your background, and your purpose, setting you apart from a sea of indistinguishable content.

Navigating Voice in Academic Discourse

In academic writing, there is a tension between maintaining objectivity and expressing individuality. Traditionally, academia has emphasized detachment, neutrality, and precision. Writers are often encouraged to remove themselves from the text, focusing on evidence and analysis rather than personal perspective. While this approach ensures credibility and minimizes bias, it can inadvertently suppress the writer's voice, especially for L2 writers who already face challenges in asserting their identity in a second language.

This long-held belief—that academic writing should be devoid of personal identity—deserves reexamination. As the boundaries of academia continue to evolve, there is growing recognition of the value of diversity, not just in the content of research but in the way it is presented. An academic paper, after all, is not just about data and conclusions; it is also about the researcher’s unique perspective, cultural lens, and interpretative approach.

Why Identity and Uniqueness Matter in Academia

1. Diversity Enriches Discourse: When writers bring their own cultural, linguistic, and intellectual backgrounds into their work, it enhances the richness of academic dialogue. Different voices lead to new interpretations and a broader understanding of complex issues.

2. Engagement and Accessibility: Academic writing can often feel distant and inaccessible. Injecting personality and individuality can make it more relatable, engaging a wider audience beyond specialists.

3. Innovation through Perspective: Uniqueness in academic writing allows for innovative ways of thinking and presenting ideas, which is crucial for fields that thrive on creativity and fresh insights.

Balancing Objectivity with Voice

To incorporate identity without compromising academic rigor, writers can:

• Show Voice Through Choices: Select specific language, metaphors, or analogies that resonate with your background or experiences, as long as they align with the academic tone.
• Use Reflexivity: Explicitly acknowledge your role as a researcher in the study. Reflexive writing can highlight your positionality and unique contribution.
• Be Strategic with First-Person Writing: While many academic traditions discourage the use of “I,” some fields now welcome it, particularly when it clarifies your role or viewpoint in the research process.
• Focus on Original Interpretation: Even in the most data-driven papers, the interpretation and argumentation reflect the writer's intellectual identity.

A Call for Change

The academic world is beginning to shift. Journals, conferences, and educational institutions increasingly value diverse voices, recognizing that true objectivity is a myth. Every piece of research is shaped by the writer’s perspective, whether it is explicit or hidden. By embracing this, academia can become a more inclusive and dynamic space.

It is time to challenge the outdated belief that academic writing must erase the writer. Instead, academic discourse should celebrate identity as a strength, allowing researchers to make their voices heard—not silenced in the name of objectivity.

Would you like to include examples of academics who have successfully integrated their unique voice into their work?

Why L2 Voice Matters

For L2 writers, cultivating a strong voice is even more critical. Writing in a second language is not just about mastering grammar and vocabulary; it’s about making your ideas resonate. Your voice conveys confidence, authenticity, and a sense of ownership over the language you are using. In academic, professional, or creative contexts, a strong L2 voice can help establish credibility and foster engagement with your audience.

Moreover, embracing your L2 voice means acknowledging and celebrating the unique perspectives you bring to the language. Non-native speakers often blend cultural and linguistic nuances that can enrich their writing, offering fresh insights that native speakers might overlook.

How to Develop Your L2 Voice

1. Know Your Audience: Tailor your tone and style to the people you are addressing. This ensures relevance while still maintaining authenticity.
2. Be Intentional with AI Tools: Use generative AI as a support, not a replacement. Edit and personalize AI-generated content to align with your voice.
3. Experiment with Style: Try different approaches to express your ideas, from informal and conversational to formal and academic.
4. Seek Feedback: Share your work with peers or mentors who can provide constructive input on how your voice comes across.
5. Read Widely: Exposure to diverse writing styles can inspire you and help you refine your own voice.

Final Thoughts

In the digital age, where content is abundant and often homogenized, having a distinct voice in writing is essential for standing out. For L2 writers, this means embracing the challenges of expressing oneself in a second language while leveraging the unique perspectives that come with it.

Your voice is your superpower—don’t let it disappear into the generic hum of AI-generated text. Let it shine, not just as an expression of your ideas, but as a reflection of who you are.

Fading Memories and Writing: Personal Reflections

Fading Memories and Writing: Personal Reflections

Janpha Thadphoothon

In this blog post, I reflect on my past experiences with writing—not just as a practice, but as a profound process of self-discovery. Through these reflections, I aim to share lessons that have shaped my journey, both for my own growth and for the benefit of my readers.

The key lessons I've learned are:

1. Writing anything, no matter how small, is always worthwhile.
2. Keeping your writings safe and accessible is invaluable.

Writing is more than a chore, an assignment to be submitted, or a mere reminder. It is a way of constructing and preserving one’s identity—a process of capturing fleeting moments and giving them meaning. Writing, in essence, becomes a bridge between memory and selfhood.

I recently rediscovered my "lost notes"—scribbles and diary entries dating back to 2001. That was a time when I was in Canberra, Australia, pursuing my doctorate. These notes, stored on my computer and preserved over the years, have proven to be both revealing and invaluable. They transport me across time and dimensions, bringing me back to those moments with remarkable clarity.

Looking back, I felt as though I had stepped into the past, as vividly as if it had all happened yesterday. I could hardly believe it—names of people I had known, classmates, friends, moments of anguish and happiness—all came rushing back, unfolding before me with remarkable clarity. It was as if these memories were as tangible as the pile of books and the computer monitor right in front of me.

These memories mean a great deal to me, and I cherish them as an integral part of my life. If someone were to offer me a pile of gold in exchange for these memories, I doubt I would make the trade. Some questions continue to occupy my mind, questions I still wrestle with—what is reality? And how do we strike a balance between freedom and structure? These reflections remain a profound part of my journey.

At first, I saw these notes as a burden—who would ever need them? Over the years, I lost many of my notes while moving between houses and apartments, and I deeply regret not having them with me anymore. Lost notes, letters, and diaries feel like fragments of myself slipping away. There’s something profoundly personal about looking at my own handwriting, seeing the crossed-out words and erased lines. They reveal how I corrected myself, revised my thoughts, and refined my emotions—a tangible record of my evolving mind and heart.

Those lost notes, letters, and diary entries, upon reflection, are a part of life’s reality—it’s not perfect. We must accept our limitations and the inevitability of some losses. Perhaps there’s a reason why certain memories fade from our existence. The names of people we once knew, the places we visited, the streets we’ll never walk again—all these hold the mysteries and beauty of life. They are, in their own way, reflections of the best parts of myself.

Writing is about creating space and expanding the dimension of time—broadening one's horizons. I am grateful to those who have taught me how to write and helped me appreciate the true charm of writing.

Janpha Thadphoothon is a lecturer of English at the Faculty of Arts, the International College, Dhurakij Pundit University (DPU) in Bangkok, Thailand.

Noises and Disorderly Mentality : The Case of Australia

 Noises and Disorderly Mentality : The Case of Australia [Work in Progress ONLY]

Janpha Thadphoothon [Work in Progress ONLY]

Noises are often distract us. In the age of information, we seem to have too much information - some would more likely considered 'noises' e.g. misleading information.

Loss of time using social media depends on how we use it for. 

The Project-Based Instruction in 2001 in Australia

 The Project-Based Instruction in 2001 in Australia

Janpha Thadphoothon

August 2001 marked a pivotal moment in my academic journey as a doctoral student in Canberra, Australia. It was a time when I discovered the transformative power of Project-Based Instruction (PBI) through my involvement in the Radio Project. This experience not only shaped my understanding of teaching and learning but also left an indelible impression on my approach to education.

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August 1, 2001: Setting the Stage

On August 1, I jotted down some notes that have stayed with me to this day:

"We helped each other set up the working timetable. As time was running out, we decided on what needed to be done and when."

At the time, I was referring to the Radio Project—a collaborative initiative that required us to plan, execute, and deliver a creative and meaningful piece of work. It was the first time I fully immersed myself in the dynamics of Project-Based Instruction, and the process was as enlightening as the outcome.

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The Beauty of Project-Based Instruction

The Radio Project introduced me to the essence of PBI: learning through doing. Unlike traditional instruction, which often emphasizes rote memorization or isolated tasks, PBI places learners in real-world scenarios where they must apply their knowledge, solve problems, and collaborate with others.

In my case, the Radio Project required us to:

• Define clear goals: We set specific objectives for what we wanted the project to achieve.
• Organize responsibilities: Each team member took on a distinct role, ensuring that the workload was balanced and manageable.
• Collaborate effectively: Communication and cooperation were essential as we navigated the challenges of producing a cohesive final product.
• Reflect on progress: Regular meetings allowed us to evaluate our progress and make adjustments as needed.

This structure taught me invaluable lessons about time management, teamwork, and the importance of adaptability—all skills that extend far beyond the classroom.

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Learning by Doing

The most profound aspect of the Radio Project was how it blurred the lines between theory and practice. While we had studied the principles of effective communication and educational technologies in class, the project required us to apply those principles in a tangible way.

For instance, we had to create scripts that were not only engaging but also educational, taking into account the needs and interests of our intended audience. This process involved brainstorming, drafting, editing, and rehearsing—all of which demanded critical thinking and creativity.

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Personal Growth

Reflecting on the experience, I realize how much I grew during this time. The project pushed me out of my comfort zone, forcing me to take risks and embrace the uncertainties that come with collaborative work. It also taught me the value of persistence—how to keep moving forward even when things didn’t go as planned.

I vividly remember the sense of accomplishment we felt when the project was finally completed. It wasn’t just about delivering a finished product; it was about the journey of learning, experimenting, and growing together as a team.

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A Lasting Impact

In my opinion, Project-Based Instruction is one of the most effective ways to foster deep learning. It engages students on multiple levels—intellectually, emotionally, and socially—and prepares them for the complexities of real-world challenges.

Looking back, I’m grateful for the opportunity to experience PBI firsthand through the Radio Project. It was a defining moment in my academic life, one that has continued to influence my teaching philosophy and practices.

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Conclusion

The Radio Project in August 2001 was more than just an academic assignment; it was a window into the possibilities of education when learning is active, collaborative, and purposeful. As an educator, I carry these lessons with me, striving to create similar opportunities for my own students to explore, experiment, and excel.

Project-Based Instruction is not merely a method—it’s a mindset, one that embraces the idea that learning happens best when we are fully engaged in the process. And for that, I will always look back on 2001 with fondness and gratitude.

The Reminiscence of My Early Academic Life

 

The Reminiscence of My Early Academic Life

Janpha Thadphoothon

During Semester 2 of 2001, I embarked on a remarkable academic journey as a doctoral student at the University of Canberra, Australia. Looking back, it was a formative year, filled with exploration, growth, and occasional moments of doubt. Back then, I was young and a bit naïve, but eager to learn and to immerse myself in the academic culture of a new environment.

I vividly remember spending most of my time attending seminars, participating in special lectures, and working on my research papers. I had a small room in Building 20 and often divided my time between the self-learning center and the library. Occasionally, I would chat with other research students and staff in the School of Languages, exchanging ideas and experiences. This rhythm of academic life was both exhilarating and overwhelming, but in my opinion, it offered the perfect balance of intellectual challenge and personal growth.

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Classes I Attended

During that semester, I enrolled in two core courses: TELL-B and Critical Pedagogy 2. These classes were scheduled on Mondays and Wednesdays, creating a rhythm that alternated between theory and practice. Mondays were dedicated to theoretical explorations, where ideas were dissected and debated. Wednesdays, on the other hand, were focused on practical applications, allowing us to see how theories could be transformed into tangible outcomes.

The course designer had intentionally crafted this dual approach to bridge the gap between abstract concepts and real-world relevance. You would agree with me that this balance is essential for any learner to grasp the true essence of academic learning.

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Reflections on the Radio Project

One of the highlights of the semester was the Radio Project, an initiative that brought together creativity, collaboration, and communication. The project challenged us to develop content that resonated with diverse audiences, requiring us to think critically and work cohesively as a team.

I remember vividly how our group meetings were filled with animated discussions about scripts, formats, and target audiences. It occurred to me that this project was not just about creating a radio show; it was a microcosm of the broader challenges of academic collaboration—listening, adapting, and contributing meaningfully.

The project also helped me develop a deeper appreciation for the role of media in education. It was through this experience that I began to see the potential of technology as a tool for empowering learners and disseminating knowledge.

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Critical Pedagogy 2: A Journey of Questioning

Critical Pedagogy 2 was another transformative experience. This course was not about easy answers but about asking hard questions. What is education for? Whose interests does it serve? How do we create spaces where learners can critically engage with the world around them?

One of the key themes we explored was the idea of empowerment through education. It occurred to me that true learning happens when students are encouraged to question and to challenge the status quo. This resonated deeply with me, shaping my approach to teaching and learning in the years to come.

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Monday, July 23, 2001: A Memorable Lecture on Time

Among the many lectures I attended, one that stands out was delivered by Ms. Ania Lian on Monday, July 23. The topic, as far as I can remember, was The Notion of Time, and it left an indelible impression on me.

The lecture began with a fundamental and thought-provoking question: “What is reality?” This seemingly simple question opened the door to a deep and engaging discussion about perception, change, and the fluid nature of time. I vividly remember grappling with the concept of reality during the lecture, trying to make sense of what it truly means.

What is real, I thought, is often what is presented to us as being real—but how can we truly know if it is? Is it merely an interpretation shaped by our senses, culture, or assumptions? These reflections led me to realize that understanding reality requires us to critically investigate the world around us. It isn’t about accepting things at face value but about questioning, probing, and seeking deeper truths.

This lecture sparked in me a habit of critical thinking and reflection that I carry to this day. It taught me that reality is not a fixed concept but a construct shaped by perspectives, and it is through curiosity and inquiry that we come closer to understanding the world and our place in it.

Ms. Lian encouraged us to think about how we perceive change. She argued that our understanding of reality is shaped by our perceptions, which are, in turn, influenced by our cultural and personal contexts. It occurred to me that this perspective was not just theoretical but deeply practical, influencing how we approach everything from research to daily life.

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Beyond the Classroom: Seminars and Conferences

Outside the structured classroom environment, I actively sought out learning opportunities. I participated in several seminars each week, ranging from intimate departmental discussions to larger public forums. Occasionally, I traveled to attend conferences at other universities, broadening my horizons and exposing myself to diverse perspectives.

One memorable event was a public seminar on educational technologies at a neighboring institution. The discussions were vibrant, and the insights I gained helped me refine my understanding of how technology could be integrated into teaching practices. These experiences reinforced my belief that academic growth is not confined to the classroom; it flourishes in the exchange of ideas across disciplines and institutions.

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Lessons Learned

Looking back on that semester, I can identify several key lessons that have stayed with me:

1. The Importance of Balance: The interplay between theory and practice, as exemplified in the TELL-B and Critical Pedagogy 2 courses, underscored the importance of balancing abstract thinking with tangible application.

2. Collaboration as a Learning Tool: The Radio Project taught me that working with others—despite the inevitable challenges—can lead to richer outcomes and deeper understanding.

3. The Value of Questioning: Critical Pedagogy 2 reminded me that education is not about memorizing facts but about cultivating a mindset of inquiry and reflection.

4. Time as a Construct: Ms. Lian’s lecture on time challenged me to rethink my assumptions about reality and change, a perspective that continues to influence my teaching and research.

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A Journey Worth Remembering

In my opinion, the experiences of Semester 2/2001 were more than just academic milestones; they were stepping stones that shaped my identity as a scholar and an educator. Back then, I was navigating a new country, a new academic system, and new ideas—all of which contributed to a transformative journey.

You would agree with me that such moments of immersion and challenge are what make academic life so fulfilling. They push us out of our comfort zones and compel us to grow in ways we never anticipated.

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Conclusion

Reflecting on my early academic life, I am filled with gratitude for the opportunities I had and the lessons I learned. It was a time of discovery, growth, and the occasional stumble, all of which have contributed to the person I am today.

As I look back, I am reminded of the words of one of my mentors: “Learning is not about arriving at answers; it’s about staying curious.” In my opinion, this curiosity is the essence of academic life—and of life itself.

Note: I rewrote and expanded my notes, and I’m glad I took them—even if they were just rough scribbles at the time.

Chomsky's Last Intellectual Debate?

Chomsky's Last Intellectual Debate?

By Janpha Thadphoothon

I am not a big fan of Noam Chomsky's political stance, but I hold great respect for his monumental contributions to linguistics and related fields. His work has shaped how we understand the human capacity for language, making him a towering figure in intellectual history. 

One of my most vivid memories is of a teacher harshly criticizing Chomsky's theory of Universal Grammar in an attempt to elevate her own. At the time, in the early 2000s, I had a limited understanding of the complex linguistic concepts involved and the importance of building upon the work of others.

I discovered Hinton's groundbreaking AI ideas through online resources. He is a highly intelligent individual, recognized as a top expert in both artificial intelligence and physics. Born in Britain, he now calls Canada his home.

Chomsky's theory of Universal Grammar (UG) and his nativist perspective on language revolutionized linguistics. At its core, UG posits that the ability to acquire language is hardwired into the human brain—a genetic endowment unique to our species. This theory stood in direct opposition to behaviorist views, famously debated in his clash with B. F. Skinner, who emphasized external stimuli and conditioning as the primary drivers of language acquisition.  

Fast-forward to the present, and Chomsky faces a new intellectual challenge. The rise of Artificial Intelligence (AI), particularly Large Language Models (LLMs) and generative AI systems like ChatGPT, has reignited debates about his theory of language. Unlike humans, these models are not born with innate linguistic knowledge. Instead, they process vast amounts of data and rely on probabilistic algorithms to generate human-like text.  

Critics argue that LLMs undermine Chomsky's nativist framework, demonstrating that language can emerge from statistical patterns and data-driven learning, without the need for an innate grammar. This debate has unfolded against the backdrop of Chomsky's advancing age—now over 95—and a rapidly evolving AI landscape.  

Chomsky has not remained silent on this issue. He has described AI systems like ChatGPT as "sophisticated tricks" that excel at mimicry but lack the deeper cognitive capacities that characterize human language. Unlike Geoffrey Hinton and other AI pioneers who see these models as a paradigm shift, Chomsky remains steadfast in his belief that true language use requires understanding, intentionality, and a biological foundation that machines inherently lack.  

This debate may well be one of the last intellectual battlegrounds for Chomsky, a scholar who has spent decades defending the biological roots of language. Whether one agrees with him or not, his enduring presence in the discussion is a testament to the profound influence he has had on how we think about human nature and communication. 

The debate between Noam Chomsky and proponents of AI advancements, including Geoffrey Hinton, revolves around fundamental disagreements on the nature of intelligence, language, and the role of generative AI models such as Large Language Models (LLMs).

Chomsky's Perspective

Chomsky argues that LLMs, such as ChatGPT, lack genuine understanding and are merely sophisticated statistical systems predicting text based on prior data. He has described such AI systems as "a trick," emphasizing that they do not engage in reasoning or possess the innate linguistic structures central to his Universal Grammar theory. Chomsky's work posits that language is an innate human capability governed by biological principles, setting it apart from AI-driven language generation, which lacks the conceptual depth and cognitive framework to mirror human linguistic competence.

Hinton and the AI Community

On the other hand, figures like Geoffrey Hinton highlight the transformative potential of LLMs, emphasizing their emergent abilities. These models, despite their lack of explicit programming to understand concepts, demonstrate skills such as contextual reasoning and stylistic imitation. Critics of Chomsky's views argue that the success of LLMs challenges the necessity of innate linguistic principles. They suggest that such systems can approximate understanding through their training on vast data sets, showing capabilities that resemble human-like behavior, even if derived differently.

LLMs and the neo-behaviorist Perspective

Hinton’s perspective on language within AI systems can be seen as echoing elements of a neo-behaviorist approach, focusing on patterns, frequency, and stimuli. In this view, the functioning of Large Language Models (LLMs) resembles the behaviorist emphasis on observable and measurable responses, as these models learn language by identifying patterns in massive datasets without requiring innate structures like Universal Grammar. This alignment is worth exploring in several ways:

Neo-behaviorist Features in LLMs

Pattern Recognition Over Cognition: LLMs process language through statistical analysis, identifying frequencies and co-occurrences of words and phrases. This approach aligns with the behaviorist principle that learning results from exposure to patterns in stimuli, rather than from innate cognitive mechanisms.

Stimuli-Response Dynamics: Behaviorists, including B.F. Skinner, viewed learning as the strengthening of responses to specific stimuli. Similarly, LLMs “learn” by adjusting weights in neural networks based on input-output pairings during training, which mirrors this behaviorist framework.

Emergence Through Data, Not Innateness: Unlike Chomsky's nativist theories that posit a biological basis for language, Hinton and others in the AI field see language as an emergent property of processing vast amounts of data. This perspective suggests that complex linguistic behavior can arise from simpler mechanisms, a hallmark of behaviorist thinking.

Where Neo-Behaviorism Diverges

Hinton’s approach differs in one critical way: while behaviorism traditionally rejected the idea of internal cognitive states, LLMs involve intricate neural network architectures. These architectures, while not biological, model internal representations that enable context-sensitive responses, suggesting a more nuanced framework than strict behaviorism.

Relevance to the Debate

In framing LLMs as neo-behaviorist, the criticism from Chomsky becomes more pointed: these systems, like behaviorist theories, may fail to capture the deeper generative and cognitive aspects of human language. The debate thus highlights whether linguistic competence is a product of surface-level pattern learning or innate faculties.

This perspective offers a valuable lens for interpreting how AI reshapes our understanding of language learning and cognition, blending modern computational techniques with echoes of mid-20th-century psychological theory​.

​

Key Debate Points

1. Understanding vs. Mimicry: Chomsky believes LLMs mimic language without understanding, while proponents argue that the models exhibit emergent properties indicating complex skill synthesis.

2. Innateness vs. Learning: Chomsky’s theory of Universal Grammar emphasizes innate structures, whereas LLM success suggests that massive data exposure and pattern recognition might suffice for language-like behavior.

3. Cognitive Boundaries: Critics, including Hinton, challenge Chomsky’s notion that AI models cannot approach human-like cognition, pointing to their practical effectiveness in real-world tasks.

Current Implications

This debate extends beyond linguistics to broader questions about AI’s role in society. The AI community acknowledges that while LLMs lack human-like intentionality, their applications in communication, knowledge synthesis, and decision-making are revolutionary. Researchers are actively exploring whether the capabilities of LLMs signify a fundamental shift in understanding intelligence or merely an extension of statistical modeling.

For more in-depth information, you can explore discussions about AI's linguistic capabilities on sites like Quanta Magazine or CBMM’s panel discussions. 

Who wins?

Predicting the "winner" of a debate between Noam Chomsky and Geoffrey Hinton on the nature of language and AI depends on how one defines "winning" and the audience's perspective. Each side represents a fundamentally different approach to understanding intelligence and language, and their strengths lie in their respective domains.

Why Chomsky Could Prevail

1. Philosophical and Biological Consistency: Chomsky’s Universal Grammar theory has withstood decades of scrutiny and is deeply rooted in biology and cognitive science. His arguments that AI lacks genuine understanding resonate with those who view language as more than just statistical patterns.

2. Critique of AI Limitations: Chomsky's critique of AI as "a trick" reflects concerns about the absence of reasoning and consciousness in models like ChatGPT. His points resonate with skeptics who prioritize human-like cognition over performance.

Why Hinton and AI Proponents Could Prevail

1. Demonstrable Results: AI systems like LLMs have produced remarkable, measurable outcomes, excelling in tasks previously thought to require human intelligence. For many, this pragmatic success outweighs philosophical objections.

2. Challenging Innateness: The ability of LLMs to generate coherent and contextually relevant language through training on large datasets challenges the necessity of innate linguistic structures, directly undermining Chomsky’s theory.

3. Broader Acceptance of Data-Driven Models: In the age of AI, data-driven approaches have gained wide acceptance for their scalability and application, making Hinton's views more appealing to technologists and applied linguists.

Who Wins? The "winner" depends on the framing:

- Academically: Chomsky’s theories hold a foundational place in linguistic thought and remain essential for understanding human language development and cognition.

- Practically: Hinton and the AI community are reshaping how society interacts with and understands language through technology.

Chomsky's Perspective on Language Acquisition

• Universal Grammar (UG): Chomsky argues that humans are born with an innate ability to acquire language, governed by a "universal grammar" hardwired into the brain. This framework provides the structures and rules necessary for language learning.
• Poverty of the Stimulus: He emphasizes that children acquire complex language structures despite limited exposure (or incomplete input), suggesting the existence of internal mechanisms that fill in the gaps.
• Critique of AI in Language Learning: Chomsky asserts that LLMs (like ChatGPT) and their statistical approaches are not analogous to how humans acquire or understand language because they lack an intrinsic grasp of grammar and semantics.

Hinton's (and AI's) Implications for Language Acquisition

• Pattern Recognition Over Innateness: Hinton’s work with neural networks implies that language acquisition might be more about recognizing and replicating patterns in large datasets (a process similar to AI training) than relying on innate mechanisms.
• Empirical Learning: Neural networks learn through exposure to massive amounts of data, resembling behaviorist theories where input (stimuli) and repetition shape learning. This contrasts with Chomsky’s claim that exposure alone is insufficient for language acquisition in humans.
• AI as a Model for Learning: Hinton’s perspective challenges Chomsky’s by showing that systems can generate meaningful linguistic output without innate grammar, calling into question the necessity of a universal grammar for learning language.

Overlap and Tensions

• The debate implicitly examines whether humans acquire language via:
  • Internal, biologically encoded rules (Chomsky).
  • External data-driven processes of pattern recognition (Hinton/AI models).

While Chomsky’s theory focuses on human-specific biological mechanisms, Hinton’s AI-driven approach suggests that learning could be explained by exposure and interaction with linguistic data. This contrast invites further exploration into whether human language acquisition is unique or shares similarities with machine learning processes.

Geoffrey Hinton does acknowledge the role of biological factors, including genetics, in language development, but his focus primarily differs from Chomsky's. Hinton’s work centers on computational models and neural networks, emphasizing the power of learning from data and experiences rather than relying on strictly innate mechanisms.

Hinton’s Recognition of Nature in Language Development

1. Brain-Inspired Models:

   - Hinton’s neural networks are based on how the brain functions, reflecting his acknowledgment of the biological foundations of intelligence, including language. These models simulate neurons and synaptic connections, inspired by human cognitive processes, which are ultimately rooted in our genetic makeup.

2. Initial Neural Capacities:

   - Hinton recognizes that humans are born with certain innate capacities, such as the structure of the brain and the ability to form connections between neurons. This mirrors a basic form of nature's contribution, though he views these as general cognitive mechanisms rather than a language-specific module like Chomsky’s Universal Grammar.

3. Adaptation Through Experience:

   - Unlike Chomsky, who emphasizes pre-wired linguistic structures, Hinton suggests that genetic predispositions provide the foundation for learning but that language itself is shaped largely by interaction with the environment and exposure to data.

Key Differences from Chomsky’s View

While Hinton doesn’t deny the influence of genetics, he diverges from Chomsky by:

- Downplaying the need for an innate, language-specific grammar.

- Highlighting the role of exposure and iterative learning in shaping linguistic abilities.

- Suggesting that human intelligence, including language, emerges from more general neural mechanisms rather than a pre-programmed linguistic blueprint.

Hinton acknowledges that nature plays a role in language development through the biological structures that facilitate learning, but he focuses on the adaptability and emergent properties of these systems. His work bridges the gap between acknowledging innate capacities and demonstrating how sophisticated learning arises primarily from data-driven processes. This creates a more empiricist view compared to Chomsky’s rationalist stance on Universal Grammar.

Ultimately, this debate may not produce a definitive "winner" because it highlights two complementary perspectives. Chomsky's work emphasizes the uniqueness of human cognition, while Hinton's contributions showcase how technology can mimic and extend aspects of intelligence. The real value lies in how these debates push the boundaries of what we know about both human and artificial intelligence.

As AI continues to advance, the question remains: does it challenge Chomsky's theories, or does it merely highlight the profound differences between artificial and human intelligence? Only time—and further debate—will tell.  

In retrospect, this controversial debate stimulated scholarly exploration and investigation. After all, such intellectual discourse is the hallmark of human civilization.

Janpha Thadphoothon is an assistant professor of ELT at the International College, Dhurakij Pundit University in Bangkok, Thailand. Janpha Thadphoothon also holds a certificate of Generative AI with Large Language Models issued by DeepLearning.AI.

Stages in AI Development and the Future of AI

Stages in AI Development and the Future of AI  

By Janpha Thadphoothon  

I am sure you would agree with me that the development of artificial intelligence (AI) is one of the most transformative technological shifts in human history. What we refer to as "artificial intelligence" is, in essence, a software application—or more accurately, a set of digital applications—that satisfies two key criteria: it can learn or be trained, and it can exhibit human-like behavior.  

I am not a data scientist but an English teacher, so my perspective on AI is not overly technical. However, I have read somewhere that AI has been evolving through distinct stages since its early beginnings. From its humble roots in the 1970s to the innovations of the 1980s, and now, as we look toward 2030, we can observe a remarkable trajectory.  

Experts in the field say that AI can be understood through a series of developmental stages, each with its unique characteristics and potential. In this blog post, I will share what I believe are five key stages in AI's evolution—from simple chatbots to what I prefer to call "agentic entities," systems that could one day manage businesses or even entire organizations.  

Types of AI and How They Learn

Before diving into the stages, let me briefly touch on the different types of AI and how they are trained.

1. Types of AI:

   • Generative AI: These systems, such as ChatGPT, DALL-E, and MidJourney, can create new content, including text, images, music, or videos. They use large datasets and advanced models to generate outputs that mimic human creativity.
   • Predictive AI: Systems like recommendation engines analyze data to predict future outcomes, such as which movies you might like or stock market trends.
   • Reactive AI: These are limited systems that only respond to specific tasks, like playing chess or diagnosing faults in machines.
   • Adaptive AI: AI capable of learning and evolving in real time, improving its performance as it interacts with its environment.

2. Training Methods:

   • Supervised Learning: AI is trained on labeled data, where it learns by example. For instance, a system might be trained on images of cats and dogs to identify which is which.
   • Unsupervised Learning: The system works with unlabeled data, finding patterns or clusters on its own. This approach is often used in market segmentation.
   • Reinforcement Learning: This involves training AI through trial and error, rewarding it for correct actions and penalizing it for mistakes. A good example is AlphaGo, which learned to master the game of Go through countless simulations.

Now, let’s look at how these training methods have contributed to the development of AI through its various stages.

The Five Stages of AI Development

1. Reactive Agents (Chatbots)  

In its earliest stage, AI is reactive, designed to handle specific inputs and generate pre-programmed outputs. These agents lack memory or the ability to understand context. They say the first chatbots, like ELIZA from the 1960s, were pioneers of this stage. Today, this level of AI is still widely used in customer service chatbots.  

2. Contextual Agents (Assistants)  

The second stage involves AI systems that can learn from data and adapt to context. Virtual assistants like Siri and Alexa fall into this category. They are smarter than simple chatbots and can perform a range of tasks, from setting reminders to answering trivia questions.  

3. Collaborative Agents (Strategists)

By the 2020s, AI began to take on more collaborative roles, assisting humans in making strategic decisions. For example, AI tools in finance or logistics analyze data and provide actionable insights. I am sure you would agree with me that such systems already show potential as strategic partners.  

4. Agentic Entities (Entrepreneurs)  

Looking toward the near future, it is believed that AI will evolve into fully autonomous systems. These "agentic entities" will be capable of managing entire enterprises, from identifying business opportunities to executing strategies. This stage could redefine what it means to lead and innovate.  

5. Networked Entities (Ecosystem Leaders)  

 In the final stage, AI systems will likely function as part of interconnected networks. They say these entities will not only work independently but also coordinate with other systems to optimize global operations in fields like healthcare, education, and transportation.  

From the 1970s to 2030: A Brief Timeline  

- 1970s: The early days of AI were driven by academic curiosity and foundational theories. ELIZA, one of the first chatbots, demonstrated the potential for AI to simulate conversations, albeit in a limited way.  

- 1980s: Expert systems emerged, allowing computers to make decisions based on pre-defined rules. This decade saw AI applications in industries like medicine and engineering.  

- 1990s-2000s: Machine learning gained traction, with systems becoming more adaptive. Breakthroughs like IBM's Deep Blue defeating a world chess champion in 1997 showcased the growing capabilities of AI.  

- 2010s: The era of deep learning and big data began. Virtual assistants like Siri, Alexa, and Google Assistant became household names. AI began assisting in areas such as autonomous vehicles and personalized recommendations.  

- 2020s-2030s: Experts predict that AI will evolve into agentic entities capable of entrepreneurship and leadership. These systems will be smarter, more autonomous, and able to navigate ethical challenges, prompting the need for robust AI regulations.  

Ethical Concerns and the Role of AI Regulations  

As AI progresses, ethical concerns inevitably arise. I have read somewhere that questions about privacy, bias, and accountability dominate discussions about AI's future. For example, who is responsible when an autonomous system makes a mistake? Can we ensure that AI decisions are fair and unbiased?  

It is believed that governments and organizations are taking steps to address these issues. AI regulations are being developed to create a balance between innovation and responsibility. For instance, the European Union has proposed frameworks to ensure AI systems respect fundamental rights and promote transparency.  

They say we are entering an age where ethics must go hand in hand with technology. Without thoughtful regulation, the potential misuse of AI could overshadow its benefits. As educators, we have a role to play in fostering discussions about these challenges and preparing the next generation to navigate this new world responsibly.  

At present, it should be clear that AI is neither a passing fad nor mere hype. It is as real and transformative as air, water, or electricity. If anyone still has doubts, I encourage them to seek the truth and explore the subject further. I cannot force people to believe in the reality of AI, but I can share my insights and experiences to raise awareness.  

The future is being shaped before our eyes, with AI playing a pivotal role alongside other beings—humans and cyborgs—working together to lead the way forward.  

Random Errors and the Event Horizon

 Random Errors and the Event Horizon

Janpha Thadphoothon

Some of the most profound questions in life come to us unexpectedly, often when we are unprepared to grapple with their depth. Many years ago, I had the privilege of conversing with two remarkable individuals whose insights shaped my understanding of randomness, design, and the nature of the universe.  

The Statistician and Random Errors  

The first individual was an expert in statistics. He once posed a question about the random error in a language (English) test I had designed. At the time, I was young and inexperienced, and I could only respond with confusion. Thankfully, he was kind, acknowledging my ignorance without judgment.  

It took me years to fully appreciate his question. I later learned that in measurement theory, a true score equals the measured score plus errors, which can be classified into two types: random errors and systematic errors. While systematic errors follow a predictable pattern, random errors are, by their nature, unpredictable and scattered.  

At the philosophical plane, the concept of randomness is intellectually stimulating. It raises questions about whether chaos truly governs the universe or whether what appears random is part of a grander design. Physicist Roger Penrose suggested that the universe might not be a product of random chance but rather of design—or something beyond our current comprehension.  

I cannot claim to have an answer to such a monumental question. However, one thing is clear: when randomness approaches zero, we encounter the realm of absolutes or singularities—a point where certainty reigns, much like the event horizon of a black hole. At this boundary, the status of things teeters in a gray zone, neither fully defined as "0" nor "1."  

The Engineer and the Event Horizon  

The second individual was an engineer specializing in telecommunications. We met at the university canteen, where I had the chance to read one of his papers on event horizons. He explained to me that in certain systems, signals encoded in the "gray areas" between 0 and 1 are secure from hacking. His work left me puzzled, as I lacked the technical background to fully grasp his ideas.  

What intrigued me, however, was his description of the "gray area" as a zone of uncertainty and transition. At the boundaries—whether in black holes or in data encoding—the usual rules break down, and we confront a state of liminality. It is a state where definitions blur and possibilities multiply.  

Randomness or Design: A Personal Choice  

This brings me back to the question that lingers: is the universe a product of randomness or design? Perhaps the answer lies within us. If you choose to see the universe as designed, you might find evidence to support that belief. If you see it as random, you might be equally justified.  

After all, we are products of this universe, composed of atoms and molecules that themselves are the outcomes of countless interactions. In a way, we embody both randomness and design—a balance of chaos and order.  

Einstein once remarked, "God does not play dice with the universe," suggesting a deterministic view of existence. Yet he also said that perhaps even God had no choice. These ideas remind us that the boundary between randomness and design may be as fluid as the event horizon, where clarity dissolves into mystery.  

In the end, the question of randomness versus design might not demand an answer. Instead, it invites us to marvel at the complexity of existence and our role within it.  

Janpha Thadphoothon is an assistant professor of ELT at the International College, Dhurakij Pundit University in Bangkok, Thailand. Janpha Thadphoothon also holds a certificate of Generative AI with Large Language Models issued by DeepLearning.AI.

How to Communicate with AI through Prompts

 How to Communicate with AI through Prompts

Janpha Thadphoothon

I'm writing this blog article in reaction to some questions students asked me in class—"Sir, what is a prompt?" The question was both surprising and refreshing. It's funny how some of the most profound questions are the simplest. The students asked in the context of using prompts to improve their writing, particularly with the help of AI tools. "You need to learn how to use prompts to work with machine (AI) agents," I advised, knowing that it would be a skill they would need sooner than later.

I hesitated to utter the term "prompt engineering." In my opinion, it sounds technical, even intimidating. I don’t consider myself an expert in prompt engineering, but I do know what a prompt is. To me, it’s essentially a command, like telling the AI, “Explain what global warming is.” AI agents, like ChatGPT or Gemini, operate according to our commands—our prompts. 

I think of a prompt as a way of communicating with the AI, shaping its response to suit our needs. You would agree with me that, when used thoughtfully, prompts are a powerful tool. They allow us to tap into AI’s vast knowledge in a way that’s personalized and useful. For instance, we could ask, “List some strategies for learning English,” and the AI could provide helpful methods, examples, and even suggest interactive exercises.

They say prompt engineering is like speaking a new language. While it may seem complex, it’s really about clarity and specificity—telling the AI precisely what we want. People often say that AI only knows what we tell it, and I think there’s truth to that. Crafting a good prompt is about understanding the details you need and then directing the AI to focus on those details.

As far as I know, using prompts effectively is about having a conversation with the AI. Think of it as guiding a partner in a dance. For example, if you’re researching climate change, you could ask the AI for “climate change data from the past decade” or “an explanation on how climate change impacts tropical ecosystems.” Each prompt guides the AI differently.

My perception is that learning to communicate with AI will be as essential as learning to write a formal letter or make a presentation. This skill opens doors to endless information and insights, empowering us to learn more efficiently. In my opinion, mastering prompts doesn’t just improve our interaction with AI; it enhances our critical thinking by teaching us to frame questions and guide conversations with purpose. 

Examples of Prompts in Communication with AI Agents

When communicating with AI, prompts can range from simple to complex depending on the desired response. Here are a few examples to illustrate how prompts work:

1. Basic Inquiry Prompt

   - Example: “What is climate change?”

   - Explanation: This is a straightforward question, and ChatGPT will typically provide a general, concise answer. It’s often referred to as a “zero-shot” prompt, meaning the AI doesn’t have any extra context or examples and must answer directly based on its training data.

2. Elaborative Prompt

   - Example: “Explain climate change in simple terms for a 10-year-old.”

   - Explanation: Here, the prompt includes additional information, requesting a response suitable for a younger audience. This guides ChatGPT to simplify complex concepts.

3. Analytical Prompt

   - Example: “Compare climate change policies in the US and Europe.”

   - Explanation: This prompt requires the AI to perform a comparative analysis, resulting in a more detailed response that considers policy differences.

4. Creative Prompt

   - Example: “Write a short story about a robot exploring a new planet.”

   - Explanation: This prompt nudges the AI to take on a creative task, generating a story rather than a factual answer.

5. Multi-Step Inquiry

   - Example: “Explain the greenhouse effect, then list three ways individuals can reduce their carbon footprint.”

   - Explanation: This prompt has multiple parts, directing the AI to provide a layered answer.

Understanding Zero-Shot Prompting vs. Multi-Layer Prompting (Structured Prompts)

Zero-Shot Prompting

Zero-shot prompting involves giving the AI a single question or command with no additional context, guidance, or examples. The AI answers based on what it “knows” from its training data.

- Example of Zero-Shot Prompt: “Summarize the plot of To Kill a Mockingbird.”

  - Here, the AI provides a direct response with no extra prompting or follow-up questions. This method is quick and straightforward but may yield a simpler response.

- Best Use Case: Zero-shot prompts work well for general knowledge questions or simple tasks that don’t require specific customization or depth.

Multi-Layer Prompting (Structured Prompts)

Multi-layer prompting, or structured prompting, breaks down a question into multiple, structured parts or layers, guiding the AI through a step-by-step approach. This approach is also known as few-shot prompting if it involves examples, or prompt chaining if it builds on previous prompts.

- Example of Multi-Layer Prompt: 

  - Layer 1: “List three major themes in To Kill a Mockingbird.”

  - Layer 2: “Now, explain each theme with a quote from the book.”

  - Layer 3: “Provide a short analysis of how each theme is relevant today.”

- Best Use Case: Multi-layer prompting is ideal for complex tasks that require deeper analysis, detailed information, or a more structured response. This method allows the AI to generate responses that build on prior information or context.

Key Differences

- Depth of Response: Zero-shot prompts often lead to brief, direct answers, while multi-layer prompts result in richer, more comprehensive responses.

- Control over Output: Multi-layer prompting gives the user more control over the AI’s output by guiding it through specific steps, whereas zero-shot relies on the AI’s interpretation of a single, isolated question.

- Application Suitability: Zero-shot is efficient for straightforward inquiries; multi-layer is better for tasks that require detailed, organized information or creative content with specific direction.

Zero-shot prompting is quick and simple but less detailed, while multi-layer prompting allows for structured, complex responses that align more closely with specific needs or goals.


When interacting with AI agents, the quality of your instructions plays a crucial role in shaping the responses you receive. Clear, specific, and well-structured instructions guide the AI, allowing it to understand your intent better and deliver results that align with your expectations. 

In other words, the way you “communicate” through prompts directly impacts how effectively the AI understands and responds. If you’re precise and provide necessary details in your instructions, the AI can generate a response that’s not only accurate but also relevant to your needs. 

For example:

- Vague Prompt: “Explain climate change.”

- Detailed Prompt: “Explain climate change in simple terms, focusing on how it affects daily life, and provide three examples of actions people can take to reduce their impact.”

The second prompt is likely to produce a more insightful and targeted response. So, yes—clear and thoughtful instructions really do matter when communicating with AI agents.

Does Politeness and Hedging Affect Responses?

From a practical standpoint, using polite expressions (like “please” or “could you…”) or hedging phrases (“I think…”, “Would you agree…?”) doesn’t impact the technical function of AI responses because ChatGPT processes the core of a question rather than emotional tone. The AI isn’t aware of politeness or human-like intentions; it simply analyzes input to generate relevant output. However, my personal experience is this - the use of politeness can actually improve interactions in specific ways. Why?

1. Clarity and Completeness: Phrasing questions as polite requests often naturally encourages you to give clearer, more specific prompts. For example, “Could you provide a list of…” often yields a better response than a vague “List…”

2. Human-Like Interaction: When AI is embedded in tools for tasks like customer service or virtual assistance, polite phrasing can feel more natural and create a sense of empathy, improving user experience for people engaging with AI in a social or professional context.

Psychological Side: Should We Treat AI as Sentient?

Regarding the deeper question of whether it’s desirable to treat AI as if it’s sentient, here are some factors to consider:

1. Social and Emotional Conditioning: Language shapes how we think and feel. If we talk to AI with human-like courtesy, we may unconsciously attribute human characteristics to it. For some people, this could create comfort, encouraging them to explore and interact more. But for others, it could blur the line between technology and true social interaction, potentially leading to misunderstandings about AI’s capabilities and limitations.

2. Empathy and Ethics: There’s a rising view that polite language may foster respectful behavior overall, even toward non-sentient systems. Teaching people to interact respectfully with AI could help reinforce empathy and patience in broader social interactions, especially for younger users. Yet, it’s crucial to remind ourselves that AI, unlike a human, doesn’t feel and doesn’t require empathy.

3. Effective Communication: Hedging and politeness often bring clarity and specificity, as we tend to be more intentional with our wording when we use polite phrases. This approach is useful, not because the AI needs it, but because it helps users articulate thoughts more clearly, leading to better AI responses.

Final Thought

In my opinion, polite and hedged language can enhance the interaction experience with AI, making it feel more human-like and approachable, which may foster exploration and creativity. However, we need to keep in mind that AI lacks true understanding, sentience, or emotion helps maintain realistic expectations and prevents us from ascribing too much human-like agency to the technology.

So, it’s effective to use polite language for our benefit in terms of clarity and comfort—but remembering AI’s non-sentient nature is key to using it wisely and effectively.

This article, as I mentioned earlier, is a reaction to a question asked by some of my students. I hope you’ve found it insightful. One key takeaway is that when you ask questions, good things happen. Curiosity opens doors to new knowledge and understanding. So, don’t hesitate—ask questions whenever you want to learn more, and seek answers from both people and AI agents alike.

About Janpha Thadphoothon

Janpha Thadphoothon is an assistant professor of ELT at the International College, Dhurakij Pundit University in Bangkok, Thailand. Janpha Thadphoothon also holds a certificate of Generative AI with Large Language Models issued by DeepLearning.AI.