To be completely honest, technical terms on a screen used to overwhelm me. I would read explanations such as “What is a domain? What is hosting? How is WordPress installed?” yet I could never retain them. One evening, I typed a very simple message: “I have no technical knowledge. I want to build my own website. Where should I start?”
There was no human on the other side, yet the responses were clear and progressed step by step. “Let’s begin by choosing a domain,” it said. Then it explained hosting, demonstrated WordPress installation, and showed when and how tools like Elementor could be useful. Whenever I was confused or stuck, I shared screenshots. Most importantly, it explained the reason behind each step. As a result, I not only learned what to do but also why I was doing it.
From the perspective of both a teacher and a learner, the foundation of this experience felt remarkably familiar. Educational psychology offers a concept known as the zone of proximal development. It suggests that a learner can reach goals with the help of a guide that would otherwise be unattainable alone. The guide provides the right support at the right time and gradually reduces assistance. Historically, this idea is well established and aligns with the classroom principle of progressing “from simple to complex.” It is also referred to as scaffolding (Vygotsky, 1978; Wood, Bruner, & Ross, 1976). My interaction with ChatGPT mirrored this precisely: the larger task was broken down into smaller segments. After each segment, a brief summary appeared, followed by a hint about the next step.
Translating this into classroom practice, instead of giving a student the full solution at once, clarifying each step and reducing support as they progress strengthens learning.
Another crucial factor was my ability to choose a format that suited me. Some days, I requested a three-point summary. Other days, I asked for a diagram. At times, a simple checklist sufficed. Universal Design for Learning (UDL) emphasizes exactly this: reducing barriers and providing multiple pathways to learning. For teachers, this means designing different routes toward the same goal while leaving room for student choice. For learners, it means progressing at their own pace and revisiting material through alternative forms of representation when necessary (CAST, 2018).
At times, I found myself stuck on trivial details late at night—for instance, I could not center a title in Elementor. I asked ChatGPT, and it provided an immediate solution with a concise explanation of why it worked. The problem was solved, but its true value lay elsewhere: instead of memorizing a quick fix, I began to grasp the underlying logic. The next day, when faced with a similar issue, I broke it down myself first and then asked a more precise question.
This shift led me to the Socratic method. A good teacher does not provide direct answers immediately but instead fosters thinking by asking timely and thought-provoking questions. Recent studies indicate that such dialogic approaches enhance comprehension and reasoning even in AI-mediated learning interactions. The model’s value lies not merely in delivering answers but in encouraging students to think critically (Chang, 2023; Zhang, Liu, & Li, 2024).
Among the most invisible yet powerful factors in this process was psychological safety. I asked the same question multiple times when dealing with complex steps. Even the fourth time I repeated myself, I felt no shame. When I missed something obvious, the environment did not judge me. This feeling mirrors the safe climate teachers strive to build in classrooms. In such climates—where mistakes are accepted as part of learning—students take more risks, ask more questions, and learn more deeply. Research has long shown how psychological safety strengthens learning behavior (Edmondson, 1999). Even in a digital conversation space, this climate reduced anxiety and boosted productivity. For students, it means the right to ask without fear; for teachers, it means opportunities to offer calm feedback and incremental corrections.
I also considered how the mind itself works. As teachers, we often want to present all the information at once. Yet working memory is limited. The more extraneous detail is added, the more the core message becomes diluted. Here, Cognitive Load Theory provides guidance: emphasize essential steps, provide examples at the right time, and reduce unnecessary load to make learning easier. I managed the conversation with this simple request: “I am a beginner, guide me step by step with explanations. I also want to understand the logic.” Receiving a clear framework quieted the noise in my mind. A similar approach is effective in the classroom: begin with the essence, follow with a fitting example, and add a cautionary note about common errors. This way, learners understand both what to do and what not to do, focusing attention where it matters most (Sweller, 1988).
The outcome of this experience was not merely the launch of a website—it was a shift in my perspective on learning. I no longer ask, “Can I do this?” Instead, I ask, “How can I learn this better?” That question serves as a compass for both teachers and students. Teachers can create opportunities to stimulate thinking with small questions even amid the rush of covering content. Students, too, can move beyond rote memorization by attempting reasoning before seeking ready-made solutions.
In short, learning is not just about collecting information; it is about embracing collaborative thinking. It is about multiplying questions. Answers will surely come, but what truly matters is that the questions keep us moving forward. If you are ready to begin, start with a single sentence. Write down your goal, what you know, and what you do not. Even writing “I know nothing” can be the starting point. From there, progress is possible. My journey began this way, and with each step, I learned more. For teachers and students alike, the most hopeful truth is this: the door to learning often opens with a simple question.
References
CAST. (2018). Universal Design for Learning guidelines version 2.2. CAST.
Chang, E. Y. (2023). Prompting large language models with the Socratic method. arXiv. https://arxiv.org/abs/2303.08769
Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://www.jstor.org/stable/2666999
Sweller, J. (1988). Cognitive load during problem solving. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4
Vygotsky, L. S. (1978). Mind in society. Harvard University Press.
Wood, D., Bruner, J. S., & Ross, G. (1976). The role of tutoring in problem solving. Journal of Child Psychology and Psychiatry, 17(2), 89–100. https://doi.org/10.1111/j.1469-7610.1976.tb00381.x
Zhang, Q., Liu, T., & Li, F. (2024). A Socratic playground for learning powered by GPT. arXiv. https://arxiv.org/abs/2406.13919
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