Artificial intelligence is no longer a distant or experimental presence in education. It now sits quietly on teachers’ desks and laptops, helping prepare lesson plans, generate explanations, summarize complex topics, and design assessment questions. From school classrooms to university lecture halls, AI-powered tools are being adopted at an unprecedented pace. Yet as this quiet revolution unfolds, a critical question demands attention: What happens when AI-generated information is wrong and is taught to students as fact?
The appeal of AI in teaching is easy to understand. A teacher pressed for time can ask an AI system to explain a difficult mathematical concept, outline historical events, or generate examples tailored to student levels. Within seconds, polished content appears. However, speed and fluency often disguise a fundamental weakness of AI systems. They do not “know” in the human sense. They predict language based on patterns, and in doing so, they can confidently produce answers that are partially incorrect or entirely false.
Consider a simple example from mathematics. A teacher preparing a lecture on limits asks an AI tool for an intuitive explanation. The response sounds convincing but subtly misstates a key condition. Students, trusting the authority of their teacher, internalize the flawed explanation. The error may not surface immediately, but months later it appears during advanced coursework, causing confusion and gaps in understanding. At that point, the damage is harder to undo.
In history or social sciences, the risks can be even greater. AI systems may oversimplify complex events, misattribute causes, or reproduce biased narratives. A lesson on colonial history generated without careful verification might omit crucial perspectives or distort timelines. When such material is presented in classrooms, misinformation gains legitimacy through institutional authority.
At the centre of this issue lies accountability. Despite the growing role of technology, responsibility for classroom knowledge has not shifted from humans to machines. Teachers remain the final gatekeepers of academic accuracy. If incorrect information reaches students, the source of the error may be technological, but the responsibility is professional.
This principle is not new. Teachers have always been expected to verify sources, whether they come from textbooks, reference books, or online materials. AI does not change this obligation. Using unverified AI content is no different from teaching from an outdated book or an unreliable website. The medium may be new, but the duty of care remains the same.
The consequences of passing on incorrect knowledge extend beyond individual lessons. In subjects like mathematics and science, conceptual errors can persist for years, undermining future learning. In professional courses, misinformation may affect practical competence. Perhaps most damaging is the erosion of trust. Students place confidence in their teachers not just as facilitators, but as reliable sources of knowledge. When that trust is broken, even unintentionally, it weakens the educational relationship.
There is also a subtler risk. Overreliance on AI can dull critical thinking. If teachers accept AI-generated content without scrutiny, students may learn to do the same. Education then shifts from inquiry to consumption, where information is accepted because it sounds correct rather than because it has been examined.
Yet rejecting AI altogether is neither realistic nor desirable. When used responsibly, AI can be a powerful educational aid. It can help teachers generate multiple explanations for diverse learners, create practice problems, or translate material into accessible language. For students, it can serve as a supplementary tool for revision and exploration. The problem lies not in use, but in uncritical use.
Many educational institutions are now recognizing the need for structured guidelines. One essential safeguard is verification. AI-generated content should be treated as a draft, not a final authority. Cross-checking with standard textbooks, peer-reviewed materials, or trusted academic sources must become routine practice. This is particularly crucial in disciplines where precision matters.
Training is another critical component. Teachers need basic AI literacy, not just in operating tools, but in understanding their limitations. Concepts such as “hallucination,” bias, and probabilistic output should be part of professional development programs. Without this understanding, even well-intentioned educators may place undue trust in AI-generated material.
Transparency can also play a constructive role. When teachers openly acknowledge the use of AI as a support tool, they model ethical and responsible practice. A simple statement such as, “This explanation was AI-assisted and then verified,” reinforces the idea that AI is a helper, not an unquestionable authority. It also encourages students to adopt a critical stance toward all sources of information.
Errors, when they occur, need not be disasters. In fact, they can become valuable teaching moments. Prompt acknowledgment and correction demonstrate intellectual honesty and reinforce academic integrity. A teacher who openly corrects an AI-related mistake sends a powerful message: learning is an evolving process, and accuracy matters more than ego.
Institutions, too, have a role to play. Schools and universities must establish clear policies outlining acceptable uses of AI, verification requirements, and accountability mechanisms. Leaving AI adoption entirely to individual discretion creates uneven practices and increases the likelihood of misuse. Thoughtful policy can provide both flexibility and safeguards.
Ultimately, the debate around AI in education is not about technology replacing teachers. It is about whether technology will amplify good teaching practices or magnify their absence. AI can enhance clarity, efficiency, and access, but it can also amplify error at scale if used without judgment.
Education has always rested on human responsibility. Long before AI, teachers made choices about what to include, what to emphasize, and what to question. Those choices shaped minds and futures. No algorithm can replace that moral and professional obligation.
As classrooms adapt to this new technological reality, the central question is no longer whether AI will be used. It already is. The real question is whether educators and institutions will insist on wisdom, verification, and accountability alongside innovation. The answer will determine whether AI becomes a meaningful ally in education or a quiet source of confusion masquerading as knowledge.
The writer is Faculty of Mathematics, Department of General Education HUC, Ajman, UAE. Email: reyaz56@gmail.com
