In the growing discussion around artificial intelligence and large language models (LLMs), the same high-stakes questions keep appearing: Are we creating an intelligence that will one day surpass us? Could it even become dangerous? These speculations draw attention, but they often sidestep the practical, immediate impact AI is having in our lives. Instead of buying into the sci-fi-level fear and speculation, we might do better to ask a different set of questions: How can these tools serve us right now? And perhaps more importantly, how do we ensure they evolve in ways that genuinely benefit people?
For many of us, the “doom and gloom” narrative around AI does little to clarify its practical uses or potential benefits. Yes, large language models are powerful, but we should be skeptical of the inflated claims that AI is on the verge of becoming an autonomous, threatening entity. As someone exploring how LLMs can be tools for language learning, instructional coaching, and professional growth, I see immense potential—just not in the ways often hyped in headlines. Let’s cut through the noise and explore the genuine, practical ways AI can enhance our lives, including its role in education and teacher training.
The deteriorating cycle of content as AI creates more entropic content
Before diving into specific uses, it’s worth addressing a key reason why AI is unlikely to become some all-encompassing, omnipotent force: it’s expensive, complex, and technically unsustainable for most purposes. High-powered LLMs require an enormous amount of computational resources to function. A single API call for some models costs around 35 cents—a tiny amount for a single use, perhaps, but this adds up when scaled. Running these models consistently, especially if AI were truly everywhere in our lives, would require astronomical costs. Even tech giants recognize this, which is why they’re investing heavily in making AI more efficient, not necessarily more powerful.
LLM Price Comparisons
The cost of AI isn’t merely financial—it’s also a matter of managing what we might call informational entropy. Harari, in his exploration of information, describes entropy as a kind of fundamental chaos that resists easy organization, and this concept applies well to the internet as a whole. The internet is a constantly shifting, ever-growing repository of information, much of it chaotic, contradictory, or repetitive. This “mechanized entropy” is what many LLMs are trained on. As these models get bigger, they consume vast amounts of this disordered content, requiring increasing power and investment to filter, organize, and make sense of it.
This struggle to “curb entropy” becomes a costly cycle, both in terms of money and energy, to the point that building infinitely larger models is unsustainable. For investors expecting high returns, the entropy of internet-trained data presents a significant problem. As AI attempts to impose order on chaotic data, the law of diminishing returns looms. Simply put, more power doesn’t guarantee better outcomes; in fact, it risks producing more of the same surface-level answers without deeper insight. Entropy being a form of classical thermodynamics.
Midjourney: information as entropy, the disorder of information and data
These technical and financial realities mean that the most effective AI applications will likely remain within targeted areas where it can genuinely enhance human capabilities without succumbing to entropy. In practical terms, this means looking at AI’s role in education, language learning, personal development, and other areas where real people can use it as a tool, not as a replacement for human intelligence.
AI as a Practical Tool for Learning and Professional Development
One of the most immediate and promising uses of AI is in areas like language learning and professional coaching. Language learning is especially suited to AI interaction because it involves conversation, repetition, and practice—areas where LLMs shine. Take, for instance, the recent addition of ChatGPT’s speech mode, which allows users to interact with the model by speaking directly. For those learning a new language, like Thai where the LLM needs to understand linguistic tones to operate as a tutor, this capability opens up an entirely new dimension of practice. ChatGPT’s instant, conversational style simulates real-life interactions in a low-stakes environment, helping learners get comfortable with tones, phrases, and pronunciation.
Beyond language, AI’s streamlined, real-time interaction has practical use in instructional coaching. Teachers, mentors, or coaches can leverage AI to prepare for meetings, brainstorm, or break down complex topics. Imagine an instructional coach working with AI to outline meeting agendas, clarify key takeaways, or provide concise advice. This capacity to deliver focused information without unnecessary elaboration could be a topic worth exploring further, perhaps even as the basis for a research paper on AI’s potential to streamline professional communication.
In both cases, AI isn’t doing something humans can’t do, but it’s enhancing the process by providing immediate, structured responses and reducing much of the repetitive or “busy work.” It’s a support system, not a replacement for human insight and judgment.
Integrating AI into Teacher Training: Practical Strategies to Overcome the “Black Box” Syndrome
For teachers to use AI effectively, they need training that goes beyond just introducing a new tool. Many educators experience “black box” syndrome—feeling intimidated by technology they don’t fully understand, which leads to reluctance or misuse. To make AI feel accessible, teacher training must demystify the technology, provide practical applications, and empower teachers to view AI as a partner in the classroom.
1. Demystify AI with Clear Explanations and Real-Life Analogies
To help teachers feel more comfortable, start by explaining AI in simple terms:
Analogy of a Librarian: Describe AI as a knowledgeable librarian who can quickly pull together information based on specific requests. It doesn’t “understand” in the way humans do but organizes data efficiently.
Focus on Patterns, Not Intelligence: Emphasize that LLMs are pattern-recognition systems, not true intelligence. This understanding helps clarify why AI responses can sometimes lack depth or nuance—they are generated based on statistical patterns in data, not genuine insight.
These explanations reduce the mystique around AI, helping teachers approach it as a tool rather than an enigma.
2. Use Real-World Scenarios for Hands-On Practice
Teachers are more likely to embrace AI when they see its relevance in their daily tasks. Structure training sessions around real-world classroom applications:
Lesson Planning and Resource Creation: Show how AI can help generate lesson ideas, prompts, and activities based on learning objectives. Allow teachers to experiment with AI to create worksheets, quiz questions, or example problems. This allows them to test the technology within the safe context of resource generation and brainstorming, rather than in front of students.
Classroom Management Scenarios: Encourage teachers to role-play classroom situations where they use AI for quick guidance or feedback. For example, they could ask the AI how to address a common behavioral issue or get suggestions for differentiated instruction in a multi-level class.
These scenario-based exercises let teachers test AI’s capabilities in a familiar context, building confidence in its role as an assistant rather than a replacement.
3. Highlight AI’s Biases and Limitations
A critical part of demystifying AI is acknowledging its imperfections. Discuss how the “entropy” of the internet data that trains AI means it can sometimes produce responses that are incomplete, biased, or off-target. Encourage teachers to:
Question the Responses: Just because an AI generates a response doesn’t mean it’s correct. By asking teachers to critically assess AI’s outputs, they build the habit of double-checking information rather than blindly trusting it.
See Themselves as Editors: Reinforce that teachers have the ultimate control over what AI-generated materials they use. AI might suggest ideas, but teachers filter, adapt, and refine those ideas to meet their specific educational goals.
This critical perspective prevents teachers from viewing AI as a one-size-fits-all solution and encourages them to use it discerningly.
4. Encourage Low-Stakes Experimentation
To overcome initial hesitations, create an “AI sandbox” where teachers can experiment with AI tools without fear of failure. For example:
Mock Scenarios: Use hypothetical classroom situations where teachers can practice using AI to answer questions, generate content, or solve problems. This lets them explore AI’s functionalities in a safe, pressure-free environment.
Peer Collaboration: Encourage small group workshops where teachers try different AI prompts together, compare results, and share insights. This peer support can make learning AI feel less isolating and more collaborative.
By practicing in low-stakes settings, teachers build familiarity with AI tools, reducing the intimidation factor.
Future Courses: Building an AI That Truly Enhances Human Potential
By training teachers in AI’s practical applications, we can overcome the “black box” syndrome, allowing educators to fully leverage AI’s capabilities. When teachers understand AI’s relationship with entropy and its practical uses, they can use it critically and thoughtfully, enhancing their teaching without being overwhelmed by technology.
If we channel our efforts into creating practical, ethically sound AI models that empower people to learn, grow, and connect, we’ll move beyond the hype and closer to the real promise of AI. A world where AI helps people learn languages, solve problems, improve their professions, and streamline their day-to-day lives sounds a lot more appealing—and realistic—than a dystopian AI takeover. In the end, it’s up to us to steer AI development in a direction that supports people, not fear.