Why Neutral AI in Education is a Myth
Samrat Biswas is a distinguished VP of Operations, Engineering, and Growth in the tech and consulting industry, renowned for his deep expertise in scaling teams and refining processes. Samrat’s writings are informed by his wealth of experience, offering readers valuable insights into the intricacies of engineering leadership, operational efficiency, and driving transformational change within organizations.
AI isn’t a future trend in edtech anymore. A 2025 HEPI and Kortext survey found that 92% of students now use AI assistance in their studies. They turn to general-purpose platforms to write assignments, summarize big lessons, and get answers to difficult questions.
At first glance, it seems like progress. Work gets done faster. Concepts get summarized instantly. Students seem more productive. But faster task completion isn’t the same as learning.
AI in education isn’t a neutral technology. It’s optimized for speed, fluency, and completion. Learning, on the other hand, depends on hard work, failure, and iterative revision. That gap turns learning from a steady process into an outcome‑driven product.
Is AI in education neutral?
If a system is neutral, it just delivers information but that’s not how general‑purpose LLMs work. They produce outputs based on the data they were trained on. Various studies show that LLMs like ChatGPT aggregate data from massive amounts of online information. That raises fundamental questions about AI neutrality in learning, education, and human knowledge. Here’s why:
1. It’s trained on uneven data
Generative AI models are trained on huge amounts of internet data. That data might look rich, but it also contains, inaccurate content, cultural biases, and all kinds of skewed patterns.
When these models are used for personalized learning, they don’t filter but they mimic the aggregate of billions of human created data points..
If a teacher uses ChatGPT to grade student assignments, the model training largely relies on online text that often misses cultural nuance or local intent. As a result, they may put students who use non‑native (americanized) English structures at a disadvantage. It may even give them lower scores, despite strong arguments.
2. It’s optimized for fluency, not accuracy
A 2025 Chegg survey found that 53% of students worry about the accuracy of AI‑generated answers. GenAI platforms like ChatGPT are designed to predict plausible text, not verify truth. Most of the time, they’re trained on mostly accurate data. But their generative nature means they can still hallucinate and produce new, inaccurate output, combining patterns from different sources.
If a student uses ChatGPT to write a 1,000‑word essay on environmental policy, the response often reads as smooth and convincing. It may even include real‑looking studies and examples. The student trusts it as authoritative and submits the essay. But verification is not the same as authorship and the student may not understand if the information was truly accurate.
3. It isn’t context‑aware in the way learning requires
General -purpose LLMs work like advanced autocomplete tools. They predict the next word based on patterns in the data they’ve learned from. These tools do not understand where the student is coming from or what their needs are. That’s why most LLM-generated responses look similar across queries, regardless of the students’ level, background, or learning gaps.
A student struggles with quadratic equations so asks ChatGPT to help solve one. The tool gives a full, step‑by‑step solution. The student copies it. The answer is correct. But on the exam day, they’re unable to solve a similar equation because they never practiced the underlying skill.
In short, the hallucinations and biases in genAI outputs come from the nature of the training data. Learning involves trying, failing, revising, and trying again. AI doesn’t follow that structure. It delivers answers without any of the struggle, compressing the process instead of supporting it.
How to navigate through the AI neutrality myth in education?
Many educators use AI-powered tutoring via general-purpose LLMs. Uses include tailoring passages by Lexile level, solving math problems, and creating questionnaires for different levels of understanding.
Although it may sound personalized, the core issue remains the same. AI still delivers complete answers before the student has engaged with the problem. The interface changes, but the underlying behavior doesn’t. Here are some strategies that can help:
1. Critically evaluate every AI output
AI can generate convincing but inaccurate information. It operates algorithmically based on the data they were trained on.
Students and educators should treat AI‑generated outputs with a critical eye. Use AI platforms like ChatGPT for assistance rather than for task execution. Before trusting a fact or claim, verify it against your course materials, subject‑matter experts, or peer‑reviewed journals.
2. Use retrieval-based tools
AI-based learning management systems built with Retrieval‑Augmented Generation (RAG) architectures are better suited for education. They retrieve relevant information from trusted sources, like research articles, PDFs, or the institution’s own syllabus, before generating output.
3. Use structured and clear prompts
The quality of AI output depends heavily on how specific your input is. Vague prompts lead to vague or inaccurate answers. Set clear expectations and give the model a structure to follow.
Use Chain‑of‑Thought (CoT) prompting. Ask the AI to explain its reasoning step by step. This helps reveal logical gaps or unsupported claims, improving accuracy and transparency during complex tasks.
4. Adjust the temperature of the AI learning tools
Temperature is a control that adjusts how creative or random the model’s responses are.
Use a low temperature (0‑0.39) for focused, factual outputs, especially for well‑defined prompts. A higher temperature (0.7‑1.0) produces more varied, creative responses, better for open‑ended tasks like brainstorming ideas or storytelling.
The Invisible Cost of Frictionless AI.
The benefits of AI in education are real. It will change how we teach, how we research, and how institutions operate. None of that is in dispute.
But AI isn't neutral. Every platform reflects the priorities it was built for, and general-purpose tools were not built for learning. They were built for speed, fluency, and completion. Learning runs on a different logic entirely, built from effort, mistakes, and the slow work of revision. That gap doesn't close because the interface is intuitive or the output sounds authoritative.
The costs aren't always visible at the moment. AI bias can disadvantage certain students without triggering any obvious failure. Hallucinated content can pass undetected through a rushed review. A student can submit polished work having done almost none of the thinking that produces understanding. None of these show up as errors. They show up later, on an exam, in a conversation, in the absence of a skill that was never actually built.
For educators and institutions, this reframes the question. It's no longer enough to ask what an AI platform can do. The more important questions are what it's optimized for, and what happens to student effort in the process. There are success stories.
Ask those questions before a tool enters your classroom. Because the platforms that feel most helpful are often the ones most worth scrutinizing. Frictionless doesn't mean harmless. It means the cost moved somewhere you're not looking.
References
Chegg. (2025, January 28). Chegg global student survey 2025: 80% of undergraduates worldwide have used GenAI to support their studies—but accuracy a top concern. Chegg.org. https://www.chegg.com/
Freeman, J. (2025, February 26). Student generative AI survey 2025. Higher Education Policy Institute (HEPI) & Kortext. https://www.hepi.ac.uk/