A Total Solar Eclipse in parts of Europe Today

Missing a rendezvous in Spain! 

 

A total solar eclipse is the most spectacular celestial phenomenon that earth-bound humans can behold

— Mabel Loomis Todd

A total solar eclipse, one of the grandest spectacles of nature, is due to take place today (12Aug26), and visible in parts of Europe* as shown in the map above.  I had planned to view it from somewhere on the plains of Spain, but it has been a vain hope.  I am missing my rendezvous because my body is not willing, though my mind is.   

[* Here is a partial list of global online livestreams where the eclipse can be watched in real time, beginning around 9 pm IST:

• ESA: https://www.youtube.com/live/DRDx2xDR8NA
• NASA: https://www.youtube.com/live/rSvCuSQhC3w
• Exploratorium: https://www.exploratorium.edu/eclipse/livestream
• Timeanddate: https://www.timeanddate.com/live/eclipse-solar-2026-august-12
• Leon, Spain: https://www.youtube.com/watch?v=eXP47SHgq3M ]

[Caution: Contrary to popular media (mis)information, this eclipse will not be visible in India, or anywhere in Asia]

Solar Eclipses

Before dwelling on the topic of the day, let me review what solar eclipses are. The following text is adapted from one of my earliest blog articles on eclipses (see here): 

Eclipses of the Sun and the Moon as viewed from any place on Earth are possible only because of a fortuitous and accidental circumstance associated with the Sun and the Moon. While the Sun is about four hundred times bigger than the Moon, it is also nearly as many times farther away from the Earth as is the Moon. Therefore, they appear to be of nearly the same apparent size (about 0.5 degree in angular diameter) as seen from the Earth. On the occasions when these three bodies are nearly in a line, solar or lunar eclipses, which may be partial or total, are possible. A partial solar eclipse results when the lunar disk hides only a portion of the solar disk on a new moon day. A total solar eclipse happens when the lunar disk is slightly larger than the solar disk and blots it out of sight from the earth at the viewing site, revealing the spectacular sight of the solar corona, which can be viewed with the naked eye. An Annular Eclipse results if the lunar disk is slightly smaller than the solar disk and a thin peripheral ring of the Sun can still be seen at maximum eclipse. 

Total and annular solar eclipses are extremely rare events at any specific place on earth and last only a few minutes at most.  For the duration of a total solar eclipse, day turns nearly into night and produces some breathtakingly beautiful effects.

With reference to the diagram below, total and annular eclipses are possible at locations in the umbral and antumbral shadow regions, and partial ones outside of them, in the penumbral shadow regions.

[From nineplanets.org]

Total Solar Eclipse of Today (12Aug26)

A total solar eclipse will occur at the Moon's descending node of orbit later today with a magnitude of 1.0386. This is happening about 2.2 days after perigee (on August 10, 2026, at 12:15 UTC).

Below is an animation showing the umbral (thick central) and penumbral shadows of the Moon sweeping over the region later today. A total eclipse will be visible at all points lying on the umbral shadow region. The penumbral region will experience partial eclipse to varying degrees.

The total eclipse will pass over the Arctic, Greenland, Iceland, Atlantic Ocean, northern Spain and extreme northeastern Portugal (see map below). The point of greatest duration will be just 45 km off the western coast of Iceland (65°10' N and 25°12' W), where the totality will last 2m 18s. The first part of the total eclipse path will, unusually, pass from east to west from Russia to Greenland, just avoiding the North Pole. A partial eclipse will cover more than 90% of the Sun in Ireland, Great Britain, Portugal, France, Italy, the Balkans and North Africa and to a lesser extent in most of Europe, West Africa and northern North America.

The total eclipse will pass over northern Spain from the Atlantic coast to the Mediterranean coast (see map below). It will be visible from the cities of A Coruña, Valencia, Zaragoza, Palma and Bilbao, but both Madrid and Barcelona will be just outside the path of totality.

The last total eclipse in continental Europe occurred on March 29, 2006 and in the continental part of European Union on August 11, 1999. The last total solar eclipse in Spain happened on August 30, 1905 and followed a similar path across the country. The next total eclipse visible in Spain will happen less than a year from now, on August 2, 2027, the day that is slated to witness the ‘eclipse of the century’ lasting well over six minutes in some parts of its path further westward along North Africa. More about this in a future blog article.

A superbly informative and interactive Google map of the event is available at:

http://xjubier.free.fr/en/site_pages/solar_eclipses/TSE_2026_GoogleMapFull.html

One of several favorable locations for observing the event is the historic and architecturally beautiful city of Leon, northwest of Madrid. For the technically inclined, the eclipse parameters at this location are shown in the map below:

The totality at Leon lasts a decent ~100 seconds, and the probability of clear skies in the late evening (looking westward) is quite high. Sunset will occur some time soon after the eclipse, thus producing an eerie effect. This should give an indication of how much I am really missing!

Tailpiece - A tale of two eclipse chase(r)s

Ten years ago, I had made extensive plans to view the total solar eclipse of 09Mar16, passing over much of a vast stretch of Indonesia, from the northern coastal city of Palu in Central Sulawesi Province.  I had communicated this to the well-known astronomer and former director of the Nehru Planetarium in Bangalore, Dr B S Shylaja, whom I had known and corresponded with for quite some time without actually meeting her face-to-face. This eclipse gave me an unexpected opportunity to meet her for the first time, in a curious way, and on foreign soil!

While holidaying in Jakarta on the way to Palu, I received a message from Dr Shylaja that she had been invited by local organizers in Palangka Raya, located southwest of Palu in Central Kalimantan Province (see map below), to talk about the eclipse and view it with them live.  I gave her some tips about the on-arrival visa process at Jakarta airport and wished her happy viewing before proceeding to Palu. 

The weather outlook in both places was good, and I had a wonderful experience as recounted in one of my earlier blogs (see here). Unfortunately, it was nearly a washout for Dr Shylaja as I later learnt from her message.

Our return flights converged at Jakarta within a span of an hour and this gave me the privilege of receiving her at the airport and spending some edifying time with her despite our sharply differing eclipse experiences. She also shared with me her home-cooked food, something equally welcome for me.  She then boarded a flight back home while I went sometime later on a flight to Singapore.

Now to today’s eclipse.  Almost a year ago, when my mind as well as my body were equally willing, I had mooted the idea of a small group of eclipse enthusiasts joining together and traveling to Leon in Spain to view what would be unfolding later today. I had successfully persuaded Dr Shylaja to join this group. Lo and behold! Now she happens to be the only one from the group stationed at the venue while the others have stayed back home for one reason or another.

Even as I write this, the weather outlook in Leon is extremely good and Dr Shylaja appears to be all set for a reversal of her 2016 experience in Indonesia. It is a matter of hours before I get confirmation from her of a successful viewing. Alas, for me it has already been a reversal of a different kind!

 

Appendix A: An Eclipse Simulator (online)

For sheer visual delight and rich graphic information*, the technically inclined reader may try out this eclipse simulator, preset for Leon, Spain.  

[*Below is a screen grab]

The event is being livestreamed from Leon at:

https://www.youtube.com/watch?v=eXP47SHgq3M

Appendix B: Totality

For a thoroughly detailed and graphic description of the unique experience of totality, I can do no better than to repeat a quote from Mark Littmann, Fred Espenak and Ken Willcox from their book The Experience of Totality, first published in 2008:

First contact. A tiny nick appears on the western side of the Sun. The eye detects no difference in the amount of sunlight. Nothing but that nick portends anything out of the ordinary. But as the nick becomes a gouge in the face of the Sun, a sense of anticipation begins. This will be no ordinary day.

Still, things proceed leisurely for the first half hour or so, until the Sun is more than half covered. Now, gradually at first, then faster and faster, extraordinary things begin to happen. The sky is still bright, but the blue is a little duller. On the ground around you the light is beginning to diminish. Over the next 10 to 15 minutes, the landscape takes on a steely gray metallic cast.

As the minutes pass, the pace quickens. With about a quarter hour left until totality, the western sky is now darker than the east, regard­less of where the Sun is in the sky. The shadow of the Moon is approaching. Even if you have never seen a total eclipse of the Sun before, you know that something amazing is going to happen, is happening now--and that it is beyond normal human experience.

Less than fifteen minutes until totality. The Sun, a narrowing crescent, is still fiercely bright, but the blueness of the sky has deepened into blue-gray or violet. The darkness of the sky begins to close in around the Sun. The Sun does not fill the heavens with brightness anymore.

Five minutes to totality. The darkness in the west is very noticeable and gathering strength, a dark amorphous form rising upward and spread­ing out along the western horizon. It builds like a massive storm, but in utter silence, with no rumble of distant thunder. And now the darkness begins to float up above the horizon, revealing a yellow or orange twilight beneath. You are already seeing through the Moon's narrow shadow to the resurgent sunlight beyond.

The acceleration of events intensifies. The crescent Sun is now a blazing white sliver, like a welder's torch. The darkening sky continues to close in around the Sun, faster, engulfing it.

Minutes have become seconds. The ends of the bare sliver of the Sun break into individual points of intense white light--Baily's Beads-- the last rays of sunlight passing through the deepest lunar valleys. Opposite the crescent, a ghostly round silhouette looms into view. It is the dark limb of the Moon, framed by a white opalescent glow which creates a halo around the darkened Sun. The corona--the most striking and unexpected of all the features of a total eclipse--is emerging.

Almost instantaneously, the incredibly thin crescent Sun fragments into a series of brilliant beads and short arcs which dwindle and vanish in rapid succession. And now, there is only one bead, set like a single dazzling diamond in a ring."

But its penetrating brilliance rapidly fades as if it were sucked into an abyss.

Totality! 

Where the Sun once stood, there is a black disk in the sky, outlined by the soft pearly white glow of the corona, about the brightness of a Full Moon. Small but vibrant reddish features stand at the eastern rim of the Moon's disk, contrasting vividly with the white of the corona and the black where the Sun is hidden. These are the prominences, giant clouds of hot gas in the Sun's lower atmosphere. They are always a surprise, each unique in shape and size, different yesterday and tomorrow from what they are at this special moment.

You are standing in the shadow of the Moon.

It is dark enough to see Venus and Mercury and whichever of the brightest planets and stars happen to be close to the Sun’s position and above the horizon. But it is not the dark of night. Looking across the landscape at the horizon in all directions, you see beyond the shadow to where the eclipse is not total, an eerie twilight of orange and yellow. From this light beyond the darkness which envelops you comes an inexorable sense that time is limited.

Now, at the midpoint in totality, the corona stands out most clearly, its shape and extent never quite the same from one eclipse to another. And only the eye can do the corona justice, its special pattern of faint wisps and spikes on this day never seen before and never to be seen again.

Yet around you at the horizon is a warning that totality is drawing to an end. The west is brightening while in the east the darkness is deepening and descending toward the horizon. Above you, prominences appear at the western edge of the Moon. The edge brightens.

Suddenly totality is over! A brilliant bead of sunlight appears. This heavenly diamond quickly grows into a band of several jewels which merge together to form the returning crescent Sun. The dark shadow of the Moon silently slips past you and rushes off toward the east.

 

Teachers Still Matter

An insightful new publication

AI is not going to replace humans, but humans with AI are going to replace humans without AI

-       Kevin Scott

 

My last blog entry (see here) carried an announcement of a forthcoming publication titled Teachers Still Matter: Foreign Language Teaching in the Age of AI and an extensive guest-written article related to it by its author Prisha Kohli. I am delighted to say that the book has since been published (see picture above) and is now available for purchase online from the following two outlets:

1.     Notion Press (publishers)

https://direct.notionpress.com/in/read/teachers-still-matter/

2.     Amazon (India)

https://www.amazon.in/dp/B0H29RPC9W/

It is also expected to be available soon at other outlets such as Flipkart.

Though the book is related to the teaching of foreign languages in the age of AI (the author is a teacher of German language and litterateur), it is highly relevant to all teachers in all disciplines as well.  This has been the rationale behind featuring it in my blog.

In a personal note, this is what Prisha has to say about her book:

After spending months researching and writing about AI, pedagogy, assessment, language learning, and the future of classrooms… I can confirm one thing:

Teachers still need coffee more than AI. ☕

Very excited to share that my book is officially launched!

Teachers Still Matter: Foreign Language Teaching in the Age of AI

This book began as one teacher asking:

“If AI can generate language instantly… what happens to language teachers?”

Somewhere between lesson plans, research papers, classroom stories, and late-night writing sessions, this book happened. And honestly, writing a book taught me almost as much as teaching itself.

If you’re a foreign language teacher, trainer, educator, language learner, or simply curious about AI and foreign language education — this book is for you.

Readers can expect a balanced, practical, and deeply human perspective on artificial intelligence in foreign language teaching. The book explains complex AI concepts in simple language that educators can immediately connect to classroom realities. It offers concrete frameworks to help teachers integrate AI without losing pedagogical control. Teachers will discover ready-to-use classroom strategies, prompts, activities, and assessment ideas from A1 to C2 levels. The book also explores major concerns such as AI-generated writing, academic integrity, over-scaffolding, and the illusion of learning. Readers will gain clarity on where AI can genuinely support teaching and where human interaction must remain central. The chapters connect established language learning theories with modern AI-supported classrooms in a practical way. Educators can expect reflection questions, classroom scenarios, and implementation templates that encourage critical thinking about technology use. Rather than promoting AI blindly or rejecting it completely, the book presents a thoughtful middle-ground approach rooted in pedagogy. Above all, the book reassures teachers that their role is not disappearing—in fact, it has become more important than ever.

Here is a concise summary of its contents:

Chapter 1 – How AI Walked into our Classrooms

AI entered classrooms quietly through everyday teaching tasks like worksheets, grammar support, and lesson preparation. The chapter explores teachers’ mixed feelings of curiosity, uncertainty, and cautious optimism about AI in education. It introduces a Model, IAMPC - Intentional AI-Mediated Pedagogical Control Model, which keeps teachers at the centre of AI-supported pedagogy. Ultimately, the chapter argues that AI can support teaching, but meaningful language learning still depends on human interaction and teacher judgment.

Chapter 2 – Understanding AI Without Technical Expertise

This chapter explains AI in simple, teacher-friendly language without requiring technical knowledge or coding expertise. It clarifies that AI works through pattern recognition and prediction, not real understanding or human thinking. Philosophical ideas from thinkers like Paulo Freire, Chomsky, and John Searle are used to show the limits of AI-generated language. The chapter reassures educators that pedagogy, empathy, and professional judgment matter far more than technological expertise.

Chapter 3 – Language Learning Theory Meets Generative AI

The chapter connects major language learning theories with the realities of AI-supported classrooms. It examines how theories like CLT, Sociocultural Theory, ZPD, and cognitive load theory still remain relevant in the age of AI. AI is presented as a scaffold that can support learning only when guided by clear pedagogical intentions. The chapter emphasizes that language acquisition still depends on interaction, negotiation of meaning, and human communication.

Chapter 4 – Teacher-Led Design with AI Support

This chapter demonstrates how teachers can intentionally design lessons where AI remains a support tool rather than the driver of instruction. It introduces practical frameworks and planning strategies for balancing efficiency with meaningful learning. Teachers learn how to create AI-supported activities while protecting learner independence and classroom interaction. The central message is that strong pedagogy must always lead technology integration.

Chapter 5 – Vocabulary and Grammar Learning in AI-Supported Classrooms

The chapter explores how AI can support vocabulary and grammar practice through personalization, examples, and feedback. It also warns against overdependence on AI-generated corrections and explanations that reduce productive struggle. Teachers are encouraged to use AI selectively to reinforce noticing, retrieval, and communicative use of language. The chapter argues that grammar and vocabulary become meaningful only when learners actively use them in context.

Chapter 6 – The Illusion of Understanding

This chapter examines how AI can create the appearance of comprehension without genuine learning taking place. Instant summaries, translations, and explanations may reduce the cognitive effort necessary for deep understanding. The chapter proposes reading models and classroom strategies that preserve inquiry, interpretation, and critical thinking. It reminds teachers that confusion, ambiguity, and struggle are essential parts of language learning.

Chapter 7 – The Silence of Perfect Writing

The chapter investigates the growing problem of polished AI-generated writing that lacks learner ownership and authentic voice. It discusses how fluent texts can hide weak linguistic control and limited understanding. Teachers are encouraged to focus more on process, drafts, revisions, and oral defence rather than only final products. The chapter ultimately reframes writing as a developmental and reflective process rather than a perfectly generated outcome.

Chapter 8 – Teaching Speaking and Interaction in AI-Supported Classrooms

This chapter focuses on speaking skills, conversation practice, and interaction in the presence of AI tools. While AI can provide rehearsal opportunities and simulated dialogue practice, real communicative competence still develops through live human interaction. The chapter highlights repair sequences, spontaneity, turn-taking, and negotiation of meaning as central to language learning. Teachers are positioned as facilitators of authentic communication rather than mere providers of language input.

Chapter 9 – Exams, Evidence, and Learning in the Age of AI

The chapter rethinks assessment practices in classrooms where AI-generated work has become increasingly common. It introduces the AI Assessment Scale (AIAS) to distinguish between acceptable support and excessive AI dependence. Teachers are encouraged to design assessments that prioritize process, independent performance, and visible thinking. The chapter argues that validity in assessment now depends on proving learner ownership, not just evaluating polished products.

Chapter 10 – Bias, Ethics, and Judgment in AI-Mediated Teaching

This chapter explores ethical concerns surrounding AI, including cultural bias, misinformation, fairness, and overreliance on automation. It emphasizes that AI outputs reflect patterns in data and may reproduce stereotypes or inappropriate language use. Teachers are presented as ethical mediators who must evaluate AI critically before bringing it into the classroom. The chapter reinforces that professional judgment cannot be outsourced to machines.

Chapter 11 – Inside the Language Classroom of 2047

The final chapter imagines the future of foreign language teaching in an AI-rich educational world. It explores how classrooms, assessment, interaction, and teacher roles may evolve over the coming decades. Despite technological advances, the chapter argues that human relationships, empathy, and pedagogical guidance will remain essential. The future classroom may look different technologically, but meaningful learning will still depend on teachers.

Conclusion – Teachers Still Matter

The conclusion brings together the book’s central argument that AI should remain a tool under teacher-regulated pedagogy. It reflects on the enduring importance of human interaction, judgment, and meaning-making in language education. While AI may transform workflows and classroom practices, it cannot replace the relational and developmental nature of teaching. The book closes with a reaffirmation that teachers remain indispensable in the age of AI.

Illustrations

Profusely illustrated, with as many as eighty diagrams, pictures, charts, cartoons, etc., the book is also rich in visual content. Below is a random sample of just a few of these:

Appendix: Contents of back cover

Blogger on the author’s work

“Teachers Still Matter: Foreign Language Pedagogy in the Age of AI” promises to be a definitive work that language educators will heartily welcome — one that brings rare wisdom and calm to a conversation too often dominated by misgivings, fear and hype. Through its groundbreaking IAMPC Model, AI Assessment Scale, and five-phase operational cycle, it places in the hands of teachers beautifully crafted, highly practical, classroom-ready, tools that restore confidence and pedagogical authority in an AI-saturated world where language teaching is no exception. Built on rigorous research spanning 250 surveyed educators and 25 in-depth interviews, yet written with unmistakable clarity and human warmth, the book speaks directly to the widely shared reality of teaching — nowhere more tellingly than in the unforgettable personal vignette about the AI avatar, which captures the anxieties of an entire profession and then, with characteristic flair, shows a clear way through. This promises to be a landmark contribution to applied linguistics and teacher education alike — a future-proof, deeply inspiring work that proves, with both evidence and eloquence, that no machine algorithm can replicate the irreplaceable human act of teaching.

Using Claude AI without letting it cloud my own judgement,

Dr S N Prasad

(Teacher Educator and Science Communicator)

An AI-edited picture of the author (left) and the blogger. 

AI cannot bridge the generational gap between them!

 

 

 

 

 

 

Beyond the AI Hype

 The Enduring Role of Teachers

 

“Teaching is not the transfer of knowledge, but the creation of possibilities for the production of knowledge.”

— Paulo Freire

Preface to another Guest Article

In the previous article, Learning in the Brave New World of AI, I had explored how artificial intelligence is rapidly transforming the landscape of education and reshaping the very meaning of learning itself. We are entering an era where access to information is no longer the primary challenge; instead, the real challenge lies in helping learners think critically, independently, and meaningfully in a world flooded with instant answers.

The conversation on this theme naturally leads to an even deeper question:

If AI can generate information instantly, what becomes of the role of the teacher?

This question sits at the heart of an upcoming book, Teachers Still Matter: Foreign Language Teaching in the Age of AI.  Its author and this blog’s latest guest writer*, Prisha Kohli (see insert below), has provided this preview of it.  As a language educator and teacher trainer, she does not see AI as the end of teaching. Rather, she believes AI is revealing more clearly than ever what truly makes teaching human.

[*I also have a very personal association with Prisha – she is my grand daughter-in-law]

From the back-cover of the forthcoming publication

AI Entered the Classroom Quietly

Interestingly, AI did not enter education through dramatic institutional revolutions. It entered quietly through teacher survival. Educators across the world began using AI tools pragmatically to reduce workload and manage growing demands. Teachers started using AI to:

• generate worksheets,
• simplify texts,
• create grammar exercises,
• produce quizzes,
• draft lesson plans,
• generate discussion prompts,
• and save preparation time.

Most teachers are not approaching AI ideologically. They are approaching it practically. And honestly, that is understandable. Teachers today are exhausted.

Educational systems often demand enormous administrative labor while providing limited structural support. AI offers efficiency in areas where teachers have long been overwhelmed. In many cases, AI genuinely helps educators reclaim time and energy. However, alongside these benefits, new anxieties have also emerged.

Teachers increasingly ask:

• How do we know students truly understand?
• What counts as authentic work anymore?
• How do we preserve independent thinking?
• How do we assess learning in an AI-mediated environment?
• Where is the boundary between support and replacement?

What is important here is that these are not technological questions. They are pedagogical questions. The real challenge is not whether AI exists. The challenge is whether education can remain intentional in how AI is used.

While these larger pedagogical questions were unfolding globally, I also found myself confronting AI much more personally inside my own teaching practice.

My Own Moment of Panic

At one point, I experimented with an AI avatar platform capable of generating instructional videos automatically. Watching a digital version of myself teach was deeply unsettling. For a brief moment, I genuinely wondered:

Am I looking at the future replacement of teachers?

The avatar could imitate my voice. It could explain grammar. It could simulate instructional delivery surprisingly well. And yet something felt absent. The more I reflected, the more clearly I understood what AI could not replicate.

AI could imitate:

• instructional delivery,
• verbal explanation,
• presentation style,
• and linguistic fluency.

But it could not reproduce the deeply human dimensions that define meaningful teaching:

• emotional responsiveness,
• relational trust,
• classroom intuition,
• contextual judgment,
• ethical sensitivity,
• spontaneity,
• encouragement,
• humour,
• empathy,
• and human presence.

Good teaching is not merely the transmission of information.

Teachers constantly make invisible pedagogical decisions:

• when to encourage,
• when to challenge,
• when to simplify,
• when to remain silent,
• when to push learners slightly beyond their comfort zones,
• and when emotional support matters more than academic correction.

These judgments emerge from human relationships, not algorithms. That experience fundamentally changed my perspective. I stopped asking whether AI could imitate teachers. Instead, I started asking whether imitation itself is enough for meaningful education. It was at that moment that I began to recognize a deeper problem emerging beneath the excitement surrounding AI in education: the growing illusion that polished performance automatically reflects authentic learning.

The Illusion of Learning

One of the most dangerous assumptions emerging in AI-driven education is the belief that fluent output automatically equals genuine understanding. Today, students can generate:

• essays,
• presentations,
• summaries,
• translations,
• reflective writing,
• and even classroom discussions

within seconds using generative AI tools.

The result often appears impressive. The language is polished. The grammar is correct. The structure feels coherent and sophisticated. But polished output does not necessarily indicate learning. This distinction is especially important in language education.

As language teachers, we know authentic learning is rarely neat or perfect. Real language acquisition involves:

• hesitation,
• uncertainty,
• self-correction,
• communicative risk-taking,
• negotiation of meaning,
• misunderstanding,
• and gradual cognitive struggle.

Learning a language is not simply about producing correct sentences. It is about developing communicative competence through repeated human interaction and meaningful use.

A student struggling to express an idea independently often demonstrates far more genuine learning than a perfectly polished AI-generated paragraph. This is because language learning is not only a linguistic process. It is also cognitive, emotional, social, and cultural.

The danger of AI in education is not merely cheating. The deeper danger is that students may begin confusing generated performance with internalized understanding.

A learner may submit an excellent essay while being unable to explain:

• why certain vocabulary was used,
• why a grammatical structure was chosen,
• or how meaning shifts across different contexts.

This creates a serious pedagogical problem. Education cannot simply measure outputs anymore. It must increasingly examine processes of thinking itself.

I remember one incident from my own classroom when the writing theme was: How do you spend time with your family?

One student submitted an exceptionally polished German article filled with advanced vocabulary and flawless grammar. Everything looked perfect — until I reached one particular sentence:

“On weekends, I passionately hunt my family in the mountains.”

Naturally, I called the student for clarification, slightly concerned about both the grammar and the family. After a very awkward conversation, we finally discovered what the student had actually intended to say:

“I enjoy hiking in the mountains with my family.”

Somewhere between AI translation and overconfident vocabulary choices, a peaceful family trekking activity had transformed into something that sounded like the plot of a criminal thriller. The essay was linguistically impressive. The communicative meaning, however, was an absolute disaster.

As these concerns about authenticity and learning continue to grow, many teachers have simultaneously begun questioning their own place within this rapidly changing educational landscape.

Teachers Do Not Need to Become Engineers

Another major concern I repeatedly encounter among educators is the growing fear that surviving professionally in the age of artificial intelligence now requires advanced technical expertise. Many teachers assume that integrating AI into education means they must learn coding, programming, machine learning, or highly specialized technological skills.

I strongly disagree.

Teachers do not need to become engineers.

What educators truly need is not technical mastery, but pedagogical clarity. The most important skills in the AI era remain deeply human ones:

• pedagogical judgment,
• ethical awareness,
• critical thinking,
• instructional intentionality,
• contextual sensitivity,
• and the ability to evaluate learning meaningfully.

In foreign language education especially, this distinction matters enormously.

AI operates through pattern prediction. It generates statistically probable language based on enormous datasets. It can imitate communication remarkably well. However, it does not possess lived experience, emotional understanding, social intuition, communicative intention, or cultural consciousness.

Language is never only grammar.

·      It is relationship.

·      It is identity.

·      It is culture.

·      It is power.

·      It is human interaction.

For example, in Spanish, the distinction between tú and usted is not simply grammatical. It reflects social relationships, emotional distance, hierarchy, politeness, and cultural expectations. Tú is generally used with friends, family members, children, or people with whom one shares familiarity and closeness. Usted, by contrast, is used in formal situations, with strangers, elders, authority figures, or in professional contexts.

A student speaking to a close friend may say:

“¿Cómo estás tú?”
(“How are you?”)

But while speaking to a professor, the same learner may ask:

“¿Cómo está usted?”

Grammatically, both sentences communicate the same idea. Socially, however, they create entirely different relationships.

These forms communicate:

·      respect,

·      intimacy,

·      hierarchy,

·      professionalism,

·      emotional distance,

·      and relational positioning.

AI may reproduce these structures correctly. But it does not truly understand their human significance. Teaching learners when, why, and how such forms are appropriate requires cultural awareness, contextual interpretation, and human judgment. That remains profoundly human work.

At the same time, this does not mean teachers can ignore AI completely. What educators increasingly need is not programming knowledge, but prompt literacy. In many ways, prompt writing is becoming a new pedagogical skill. A poorly designed prompt often produces superficial, inaccurate, culturally inappropriate, or cognitively weak materials. A well-designed prompt, however, can generate highly targeted classroom support materials within seconds.

The difference lies not in technical expertise, but in pedagogical thinking.

For example, many teachers initially write prompts like:

❌ Bad Prompt Example 1
“Create a German worksheet for class 8th.”

This produces vague and often unusable output because the learning objective is unclear.

A stronger pedagogically guided version would be:

✅ Good Prompt Example 1
“Create a CEFR A1 German worksheet for Indian adult beginners practicing separable verbs in daily routines. Include:

• 10 gap-fill exercises,
• 5 speaking questions,
• Kannada transliteration support,
• and one communicative pair activity.”

The second prompt reflects instructional intentionality. The teacher clearly defines:

• level,
• learner profile,
• linguistic target,
• classroom purpose,
• and activity type.

Similarly:

❌ Bad Prompt Example 2
“Explain German grammar topic conjunctions.”

This is too broad and pedagogically meaningless.

A more effective version would be:

✅ Good Prompt Example 2
“Explain the difference between weil and denn for A2 learners using simple examples related to school and family life. Include common learner mistakes and a short practice activity.”

Again, the improvement comes not from technical skill, but from pedagogical precision.

Another common example:

❌ Bad Prompt Example 3
“Make conversation questions for B1 level.”

This often generates random, repetitive, or culturally disconnected questions.

A better alternative might be:

✅ Good Prompt Example 3
“Generate B1-level role-play speaking tasks for nurses preparing for work in Germany. Focus on patient communication, empathy, and formal language use in hospital contexts.”

This is where teachers remain irreplaceable.

AI may generate language.

But teachers define:

• what matters,
• what is appropriate,
• what aligns with learner needs,
• what supports development,

The future of education therefore does not belong to teachers who become engineers. It belongs to teachers who remain intellectually curious, pedagogically reflective, and critically aware of how technology should serve learning rather than dominate it.

Yet, even when students and teachers learn to use AI thoughtfully, a far more difficult challenge still remains unresolved: how do we evaluate learning fairly in an AI-mediated world?

The Real Crisis Is Assessment

Perhaps the greatest challenge artificial intelligence creates in education is not content generation itself. The real crisis is assessment.

For generations, educational systems across the world have relied heavily on polished final products as evidence of learning. Essays, assignments, homework, projects, presentations, and take-home tasks have traditionally functioned as visible indicators of student understanding. The assumption behind these systems was relatively straightforward: if a student could produce sophisticated work independently, then meaningful learning had likely occurred.

But generative AI fundamentally disrupts that assumption.

Today, students can produce highly polished essays, accurate summaries, grammatically sophisticated responses, and even reflective writing within seconds using AI tools. As a result, polished output alone no longer reliably demonstrates independent competence. A beautifully written essay may reveal very little about whether the learner actually understands the ideas, can explain them independently, or could reproduce similar thinking without technological assistance.

This creates a profound educational dilemma.

The problem is not merely that students may “cheat.” The deeper issue is that traditional assessment systems were designed for a world in which producing polished text required visible cognitive effort. AI has now separated product from process. Students may successfully complete tasks while bypassing many of the intellectual struggles through which genuine learning traditionally develops.

This forces educators to rethink a much more fundamental question:

What does assessment actually measure?

In my own work on foreign language pedagogy and AI, I increasingly argue that future assessment must move beyond static products and focus far more on visible thinking processes. The central concern can no longer be whether students simply produce correct answers. Instead, educators must design assessments that reveal how learners think, adapt, communicate, and respond in real time.

This includes greater emphasis on:

• oral defense,
• spontaneous interaction,
• explanation,
• reflection,
• paraphrasing,
• adaptation,
• communicative flexibility,
• and real-time performance.

For example, a student may submit a flawless foreign language essay generated partially through AI support. But can that same learner explain the vocabulary choices orally? Can they paraphrase their own sentences spontaneously? Can they adapt their ideas when the communicative context changes? Can they sustain authentic interaction without technological mediation?

These questions reveal something far more important than surface-level correctness.

The key educational question is therefore no longer:
“Can the student produce an answer?”

The more important question becomes:
“Can the student think independently beyond generated responses?”

This distinction matters enormously because education has never been simply about information retrieval. Human learning is not equivalent to accessing answers quickly. Education is ultimately about developing individuals capable of:

• reasoning,
• adapting,
• communicating,
• questioning,
• solving problems,
• and eventually functioning independently in the world.

Yet competence rarely develops without cognitive effort. Struggle, uncertainty, revision, misunderstanding, and gradual improvement are not obstacles to learning; they are often the very mechanisms through which learning occurs. When AI bypasses productive struggle entirely, students may complete academic tasks successfully without actually developing durable internalized understanding.

This is especially dangerous in language learning, where communicative competence depends not only on recognition, but on active control under unpredictable human conditions.

Ironically, as AI complicates assessment and exposes the limitations of traditional educational models, it is also revealing something unexpected: the uniquely human dimensions of teaching have become more visible than ever before. That is why teachers matter more now, not less. No algorithm can fully replace that deeply human developmental process.

AI Makes Human Teaching More Visible

Ironically, the rise of artificial intelligence may be helping society recognize the true value of human teachers more clearly than ever before. For decades, many people misunderstood teaching as the simple transfer of information from one person to another. In such a model, education appeared replaceable: if information could be digitized, stored, and delivered efficiently, then perhaps machines could eventually assume much of the teacher’s role. But the emergence of generative AI has exposed the limitations of that assumption. When machines can instantly generate explanations, summaries, translations, exercises, and even entire essays, we are forced to ask a deeper question: What exactly makes teaching human?

The answer lies in everything education was always meant to be beyond information delivery.

AI can generate language, but it cannot genuinely care whether a student is discouraged after repeated failure. It cannot truly recognize the silent anxiety of a learner afraid to speak in front of classmates. It cannot sense the emotional hesitation of a beginner struggling to pronounce unfamiliar sounds in a foreign language classroom. Human teachers can. True language learning involves identity, culture, confidence, hesitation, humour, tone, politeness, misunderstanding, repair, and emotional risk-taking. It requires learners to participate in human interaction, not simply produce linguistically accurate output.

Students therefore need far more than information.

·      They need guidance when they feel lost. They need encouragement when progress feels slow.

·      They need constructive feedback that understands not only what is wrong, but why the learner made that mistake.

·      They need motivation to continue despite frustration.

·      They need someone who notices improvement even before they notice it themselves.

Most importantly, they need someone who believes in their ability to grow.

AI can simulate supportive language patterns. It can produce phrases that sound encouraging. But simulation is not the same as genuine relational presence. A machine does not truly invest emotionally in a learner’s development. Teachers do.

Human teachers build classroom cultures that shape how students experience learning itself. They create emotional safety where mistakes become part of growth rather than sources of humiliation. They mediate conflict, encourage participation, manage group dynamics, and adapt explanations based on individual personalities and emotional states. They recognize confidence, hesitation, boredom, curiosity, and frustration through subtle human cues that machines fundamentally cannot interpret with genuine understanding.

In many ways, AI is clarifying the role of teachers rather than diminishing it. As machines increasingly handle routine informational tasks, the human dimensions of education become more essential, not less. The teacher’s role shifts away from being merely a provider of information toward becoming a mentor, designer of learning experiences, ethical guide, motivator, and facilitator of human development.

The future of education therefore is not a competition between humans and machines. It is a reminder that education was always human at its core. And in an increasingly automated world, that humanity may become the most valuable educational resource of all.

Recognizing the enduring importance of teachers allows us to move beyond simplistic debates about humans versus machines.

The Future Is Not Human vs. AI

The future of education is not a battle between humans and artificial intelligence. It is not a choice between traditional teaching and technological innovation. I do not believe the future lies in rejecting AI entirely, nor do I believe it lies in surrendering completely to automation. The future depends on intentional pedagogy — pedagogy in which technology remains guided by human judgment, educational purpose, and ethical responsibility.

Artificial intelligence is already transforming classrooms across the world. Language teachers today can generate reading materials in minutes, simplify difficult texts for weaker learners, create differentiated worksheets, produce vocabulary lists, design pronunciation practice, and even simulate conversational activities using AI-powered tools. Used thoughtfully, AI can become an extraordinary educational support system. It can support differentiation, accessibility, scaffolding, brainstorming, rehearsal, material creation, and feedback. For students who struggle with confidence or require additional practice outside the classroom, AI can offer opportunities that were previously difficult to provide at scale.

Yet the presence of AI also forces us to confront an important question: What is the role of the teacher when information is instantly available everywhere?

My answer is simple: teachers matter more now, not less.

Technology can generate content, but it cannot truly understand the learner sitting in front of it. It cannot fully perceive hesitation in a student’s voice, recognize emotional withdrawal, sense confusion hidden behind silence, or decide when a learner needs encouragement rather than correction. Teaching is not merely the transfer of information. It is the creation of conditions in which learning becomes possible.

I experienced this very clearly in one of my German language classes. A student preparing for a B1 speaking examination used AI tools extensively to generate model answers. On paper, her responses looked impressive — grammatically correct, sophisticated, and polished. However, during classroom interaction, she struggled to answer spontaneous follow-up questions. When asked to explain her own ideas differently, she became hesitant and dependent on memorized patterns. The AI had helped her produce language, but it had not helped her internalize it. At that moment, my role as a teacher became essential. Instead of allowing the student to continue relying on generated perfection, I redesigned activities that focused on spontaneous communication, negotiation of meaning, and real interaction with classmates. Slowly, confidence and authentic control began to emerge. The problem was not AI itself. The problem was the absence of pedagogical regulation.

In another classroom, however, AI became genuinely transformative. I worked with a mixed-level group where some learners struggled significantly with reading comprehension. Using AI tools, I was able to adapt the same German text into multiple difficulty levels within minutes. Stronger learners worked with the original authentic version, while weaker learners received scaffolded vocabulary support and simplified sentence structures. This allowed all students to participate in the same classroom discussion without feeling excluded or overwhelmed. In this case, AI did not replace teaching; it strengthened differentiated instruction under teacher guidance.

These examples reveal an important truth: AI is neither inherently liberating nor inherently dangerous. Its educational value depends entirely on how teachers design learning around it.

That is ultimately the central message behind my upcoming book, Teachers Still Matter: Foreign Language Teaching in the Age of AI. The rise of AI does not reduce the importance of teachers. It clarifies why they matter. Because education has never been only about delivering information. Education is about helping human beings think, struggle, communicate, grow, question, and eventually become independent.

And no matter how sophisticated technology becomes, that human work cannot be fully automated.