AI Is Changing How Students Learn
Generative AI has rapidly become part of everyday student life. Learners use it to understand unfamiliar concepts, brainstorm research questions, improve study plans, receive feedback, and prepare for assessments. Used responsibly, AI can give students something that is often difficult to obtain in a large class or online course: immediate, personalized support. A learner who does not understand a statistical concept, for example, can ask for a simpler explanation, a practical example, and a short knowledge check.
However, the same technology can produce complete essays, solve problems, and generate convincing but potentially inaccurate references. This creates an important distinction. AI can either support the learning process or replace it. The most useful question for educators is therefore not whether students should use AI tutoring. It is how learning activities should be designed so that AI strengthens independent thinking.
Effective AI Tutoring Requires Good Learning Design
Emerging research illustrates why Instructional Design matters. A 2025 study published in Scientific Reports examined a carefully designed AI tutor in an undergraduate physics course. Under the conditions studied, students learned more in less time and reported stronger engagement than students attending an in-person active-learning class. This does not mean that any chatbot automatically improves learning. The AI tutor was structured around defined learning objectives, appropriate scaffolding, and established teaching principles.
Another large field experiment on generative AI and learning found a different result. Students with access to an unrestricted AI assistant performed better during practice, but some performed worse when the AI was removed. A more carefully designed AI tutor with learning safeguards reduced this negative effect. Together, these findings suggest a practical principle: AI tutoring is most valuable when it behaves like a tutor rather than an answer machine.
Introducing The LEARNT Model
The LEARNT model is a six-step framework that educators, online tutors, and learning designers can use when incorporating AI tutoring into academic and professional learning.
1. Locate The Learning Outcome
Before opening an AI tool, students should identify what they are expected to learn. An assignment might require a student to evaluate competing theories rather than simply describe them. A dissertation task might require the learner to develop a defensible research question. A professional safety activity might test whether someone can identify hazards and justify suitable controls. Without a clear learning outcome, students can easily focus on producing polished text instead of developing the required knowledge. Educators can support this step by translating assessment requirements into clear questions:
- What should the student understand?
- What decision should the student be able to justify?
- Which skill must the student demonstrate independently?
- What evidence will show that learning has occurred?
2. Engage Independently Before Using AI
Students should make an initial attempt before asking AI for help. This might be a short outline, an explanation in their own words, a proposed research question, or an initial solution to a problem. The first attempt does not need to be perfect. Its purpose is to activate prior knowledge and expose gaps in understanding. A student planning a dissertation, for example, could first write:
- The proposed research problem.
- The intended population or context.
- The main variables or concepts.
- The reason the topic is worth investigating.
- Two possible research questions.
The student can then compare this thinking with the feedback provided by AI tutoring. Starting with AI-generated content makes it harder to determine what the learner genuinely understands.
3. Ask For Guidance Instead Of Final Answers
The quality of an AI-supported learning activity depends heavily on the type of assistance requested. Prompts that ask AI to produce a complete assignment remove the intellectual work that assessments are intended to measure. Coaching prompts are more valuable because they keep the student responsible for the final decisions. Useful prompts include:
- Do not write the answer for me. Ask five questions that will help me evaluate this argument more critically.
- Review my outline against these assessment criteria. Identify missing reasoning, but do not rewrite the outline.
- Explain this concept at three levels: beginner, undergraduate, and postgraduate. Finish with two questions that test my understanding.
- Identify statements in my draft that require evidence. Do not create or recommend references.
- Quiz me one question at a time and explain why my answers are correct or incorrect.
These instructions position AI as a questioning partner, formative reviewer, or practice tutor.
4. Review The Evidence And Reasoning
AI-generated information should never be treated automatically as reliable. Language models can produce incorrect facts, outdated guidance, distorted summaries, and references that do not exist. Students should verify important claims using original, credible sources. For dissertation and thesis research, this normally means consulting academic databases, peer-reviewed publications, official statistics, professional standards, and primary documents. A responsible verification process should include the following questions:
- Does the cited source exist?
- Does it contain the claim attributed to it?
- Is the source current enough for the subject?
- Is it a primary source or merely a summary?
- Are important limitations or opposing findings missing?
- Has the student read the complete source rather than relying on an AI summary?
This step also develops information literacy, which is increasingly important as AI-generated material becomes harder to distinguish from verified knowledge. The UNESCO AI Competency Framework for Students similarly emphasizes critical judgment, ethical awareness, and responsible participation in AI-supported environments.
5. Note Revisions And AI Use
Students should record how AI contributed to their work. A simple decision log can include:
- The original idea or draft.
- The question asked of AI.
- The feedback received.
- What the student accepted or rejected.
- Why the student made that decision.
- Which information was independently verified.
Documenting the process discourages uncritical copying and makes student reasoning more visible. It can also help supervisors provide better feedback because they can see how the project developed. Institutions should give students clear instructions about when AI disclosure is required. Expectations should be specific to each activity because acceptable support during brainstorming may not be acceptable in a final examination or assessed report.
6. Test Independent Performance
The final test of AI-supported learning is whether the student can perform without AI. After using AI to study a topic, learners could:
- Explain the concept without looking at notes.
- Complete a new problem independently.
- Defend their research choices in a short discussion.
- Produce a timed outline from memory.
- Identify weaknesses in an unfamiliar argument.
- Apply the principle to a different professional scenario.
If a student can produce an impressive document with AI but cannot explain its reasoning, meaningful learning has probably not occurred.
Applying The Model To Dissertation And Thesis Learning
Consider a postgraduate learner developing a dissertation research question. The learner begins by defining the intended learning outcome: producing a focused and researchable question supported by a clear academic rationale. The student then writes an initial question independently. Instead of asking AI to select a topic or write a proposal, the student asks it to identify ambiguity, hidden assumptions, variables that require definition, and practical limitations.
The student searches appropriate academic databases and checks each suggested issue against published literature. After revising the question, the student records which changes were made and why. Finally, the learner explains the research question, methodology, and expected contribution without using AI.
The intellectual ownership remains with the student, while AI supplies structured formative feedback. The same process can support individual assignments, literature reviews, thesis planning, workplace learning, and professional qualification preparation.
What eLearning Designers Should Provide
Telling students to "use AI responsibly" is too vague. Effective online courses should provide practical boundaries and learning structures. Course designers can help by:
- Defining permitted and prohibited AI uses for each activity.
- Supplying example coaching prompts.
- Requiring independent first attempts.
- Teaching students how to verify AI-generated claims.
- Assessing research decisions and reflection as well as final outputs.
- Including opportunities for oral explanation or independent application.
- Protecting confidential, personal, and organizational information.
- Providing alternatives for students who cannot or do not wish to use AI.
Educators should also consider accessibility and digital inequality. Some students have access to advanced paid systems, while others rely on limited free tools. Essential learning outcomes should not depend on purchasing a particular AI product.
From Faster Output To Deeper Learning
AI tutoring can help students improve academic performance, but better grades should result from better understanding rather than automated completion. The most effective AI-supported learning asks students to think first, question actively, verify evidence, document decisions, and demonstrate that they can perform independently. When these safeguards are built into eLearning, AI becomes more than a shortcut. It becomes a tool for feedback, reflection, practice, and intellectual development.
