AI tools in the IB classroom can support explanation, differentiation, formative feedback, and practice without compromising academic honesty. The essential boundary is that AI may support a student's learning, but it must not replace the thinking, decision-making, or authorship an assessment is designed to measure.
The IB does not impose a general ban on artificial intelligence. Instead, teachers must distinguish legitimate learning support from assistance that undermines authorship, while following subject guides, school policy, data protection requirements, and task-specific conditions.
What the IB officially says about AI
The IB's guidance on AI in learning, teaching, and assessment treats generative AI as a technology students should learn to use critically. Students need to investigate bias, verify factual claims, compare outputs with reliable sources, and recognize that fluent language does not guarantee accurate information.
The central integrity rule is clear: AI-generated material is not automatically the student's own work. If permitted AI-generated text, images, graphs, or other material appears in assessed work, the contribution must be clearly identified and appropriately referenced. Presenting generated material as original student work can constitute academic misconduct.
Appendix 6 of the IB Academic integrity policy explains that copied or paraphrased AI output should be acknowledged in the text and bibliography. Relevant details may include the tool, prompt, and date of generation, recorded according to the school's referencing system.
Citation does not make every use acceptable. An acknowledged AI-written analysis may still fail to demonstrate student achievement, or the assessment may prohibit such assistance. Teachers must consider both attribution and authorization.
A traffic-light framework for classroom AI use
The purpose of the task should determine how much AI assistance is appropriate. AI can have a larger role during low-stakes learning, but that role should narrow when work contributes directly to formal assessment.
| Risk level | Appropriate examples | Necessary controls |
|---|---|---|
| Green: learning support | Explaining a concept, defining a command term, generating retrieval questions, or giving one hint on a practice problem | Students verify the explanation and complete a similar task independently |
| Amber: supervised assistance | Brainstorming approaches, identifying unclear reasoning, or commenting on a practice response | Teachers define the permitted role, retain process evidence, and require students to explain their decisions |
| Red: assessment replacement | Writing IA paragraphs, selecting an EE argument, producing TOK analysis, interpreting results, inventing citations, or completing a test | Prohibit the use and apply the school's academic integrity procedure when necessary |
This is a practical classroom model, not an official IB classification. The same use may change category according to context. Generating questions during revision may be green, while receiving answers during a controlled assessment is red.
Five questions to ask before permitting AI
Teachers can make consistent decisions by asking:
- What learning should the task reveal? If students are being assessed on evaluation, AI must not construct that evaluation for them.
- Is the assistance explicitly permitted? Check task instructions, school policy, the current subject guide, and assessment-specific rules.
- Who makes the intellectual decisions? Topic selection, methodology, interpretation, argument, evaluation, and reflection should remain with the student when assessed.
- Can the student demonstrate the learning independently? Use oral questioning, closed-tool writing, or a comparable unfamiliar problem.
- Can the interaction be documented safely? Students should record the tool, date, purpose, prompts, output used, verification, and resulting decisions.
A useful departmental principle is that AI may reduce unnecessary friction, but it must not remove the productive difficulty through which learning becomes visible.
Productive uses of AI tools in the IB classroom
Explanation, practice, and feedback
AI can restate a concept at different levels, provide another example, or compare two methods. This may help multilingual learners or students needing additional explanation, but outputs should be checked against teacher-approved materials.
Effective prompts request guidance rather than completion. For example: “Explain why this step is incorrect, then give me one similar problem without solving it.” The student should then close the tool and apply the explanation independently.
AI can also support low-stakes formative practice after students make an initial attempt. It may identify a missing step, ask diagnostic questions, or suggest a concept to revisit without supplying submission-ready prose. RevisionDojo's guide for IB teachers and departments explains how Questionbank practice, class analytics, and Jojo AI can support this feedback cycle, although automated feedback remains advisory rather than an official IB mark.
Critical AI literacy
Teachers can make unreliable output an object of analysis. Students might identify unsupported claims, detect bias, verify references, compare cultural perspectives, or explain why an apparently confident response is misleading.
This is especially relevant to TOK because it raises questions about authority, evidence, interpretation, and knowledge production. The IB's AI questions and concerns guide encourages students to examine bias and use AI alongside other sources rather than treating it as a source of truth.
Higher-risk uses in IA, EE, and TOK work
Coursework requires greater caution because student ownership must remain visible. AI should not choose the research question, perform the central analysis, interpret findings, manufacture data, write reflections, or produce polished sections for submission.
A defensible process has four stages:
- The student produces an independent plan or attempt.
- AI provides limited diagnosis or general explanation, if permitted.
- The student chooses what to change and rewrites independently.
- The student records the interaction and can explain every decision.
RevisionDojo's guidance on ethical AI use in IB coursework and IA feedback and moderation clarifies this distinction. Jojo AI and Coursework Review should provide diagnostic support, while teachers retain responsibility for permitted feedback and students retain authorship.
Design assessments that make learning visible
Attempting to identify AI-written work only after submission is unreliable. Better assessment design captures the student's thinking throughout the process through proposals, annotated sources, plans, data records, drafts, checkpoints, and short process reflections.
Teachers can also use viva-style conversations, supervised writing, local or class-specific evidence, and fresh transfer questions completed without AI. These measures reveal whether students understand the claims, vocabulary, methods, and evidence in their work.
An automated AI-detection score should never be treated as proof of misconduct. Concerns should be investigated through normal school procedures using version history, source records, previous work, task conditions, and the student's explanation.
Protect student data and establish a protocol
Before approving a tool, schools should examine what data it collects, where information is stored, whether uploads are used for training, and how records can be deleted. Students should not upload personal information, confidential documents, unreleased assessment materials, other students' work, or complete coursework drafts to unapproved services.
UNESCO's guidance for generative AI in education and research emphasizes privacy, age-appropriate use, institutional validation, and human agency. Local law, safeguarding rules, platform age requirements, and consent procedures also apply.
For each significant task, teachers should label AI use as prohibited, limited, or permitted. A brief AI-use log should record the tool, date, purpose, important prompts, output accepted or rejected, verification sources, and independent changes. RevisionDojo's academic integrity guide for IB teachers offers further routines for documenting process and maintaining independent practice.
Common mistakes to avoid
A total ban may drive AI use out of sight, while unlimited access can quietly replace independent thought. Other mistakes include assuming citation makes all assistance permissible, leaving permitted use undefined, accepting invented references, uploading sensitive work, and trusting automated marks without checking current criteria.
Departments should also avoid contradictory rules between subjects unless assessment differences are clearly explained. Consistent language helps students understand that acceptable assistance depends on purpose and conditions, not simply on the tool used.
Conclusion
Responsible AI use depends on purpose, permission, ownership, transparency, verification, and independent demonstration. AI is most useful when it explains, questions, diagnoses, and supports practice; it creates the greatest integrity risk when it writes, decides, interprets, or completes assessed work.
A shared protocol, visible process evidence, and careful assessment design are stronger safeguards than surveillance alone. RevisionDojo can support this approach through Jojo AI, Questionbank, class analytics, and Coursework Review, provided teachers retain professional judgment and students retain authorship.
Sources and referenced URLs
- IB guidance on artificial intelligence
- IB Academic integrity policy
- IB artificial intelligence questions and concerns
- UNESCO guidance for generative AI
- RevisionDojo for IB teachers and departments
- Ethical AI use in IB coursework
- RevisionDojo IA feedback and moderation
- RevisionDojo academic integrity guide