[AI SHAPERS 2025] AI Agent to Create Multiple-Choice Questions - Nadim Mottu

The integration of artificial intelligence (AI) into education opens up fascinating possibilities, particularly in the creation and distribution of educational materials. Among the promising applications, the idea of AI agents capable of automatically generating multiple-choice quizzes (MCQs) from university lecture notes represents a significant advancement. Such a tool could transform how students practice and how teachers design their assessments.

Imagine a scenario where a student, after attending a lecture and taking detailed notes, could submit this material to an AI agent. This agent, equipped with advanced natural language processing (NLP) and semantic understanding capabilities, would analyze the content of the notes, identify key concepts, important definitions, cause-and-effect relationships, processes, and concrete examples.

With this understanding, the agent would be able to formulate a series of relevant and varied questions, each accompanied by several answer options, only one of which is correct.

The complexity of this task should not be underestimated. The AI agent would not simply identify keywords and construct trivial questions. It would need genuine contextual intelligence to grasp the nuances of academic discourse, subtle arguments, and implicit connections between the various ideas presented in lecture notes. Furthermore, the quality of the generated multiple-choice questions would crucially depend on the AI's ability to formulate plausible distractors—that is, incorrect answer options that are nonetheless close enough to the truth to encourage reflection and genuinely test the student's understanding.

The potential advantages of such a system:

• For teachers , such an AI agent could significantly reduce the workload associated with designing exercises and formative assessments. They could focus more on teaching, interacting with students, and personalizing their instruction, while having a powerful tool at their disposal to generate high-quality multiple-choice questions in less time. Furthermore, the AI could potentially suggest questions that target specific aspects of the course that the teacher might not have intuitively considered.

  
  

• For students , this would offer an inexhaustible source of self-assessment exercises, personalized according to the specific content of their own notes. They could thus test their understanding as they learn, identify their weaknesses, and reinforce their knowledge independently. This would foster a more active and engaged learning approach.

Implementing such a system, however, raises several technical and pedagogical challenges. The quality of the recognition and interpretation of lecture notes, which can vary considerably in terms of structure, clarity, and level of detail, is a major obstacle. The AI would need to be able to handle a wide variety of writing styles and discipline-specific terminology. Furthermore, it would be essential to ensure that the generated multiple-choice questions are relevant, balanced, and that they genuinely assess in-depth understanding rather than mere memorization.

Another crucial aspect concerns validation and human supervision. It is unlikely that an AI agent could operate completely autonomously, at least in the near future. Teacher intervention would be necessary to verify the relevance and quality of the generated multiple-choice questions, to ensure they align with the course's learning objectives and are free of errors or ambiguities. AI could thus act as a powerful assistant, but the ultimate pedagogical responsibility would always rest with the human.

Furthermore, the question of adapting to different academic disciplines is fundamental. Types of knowledge and modes of reasoning vary considerably from one field to another. An AI agent designed to generate multiple-choice questions in theoretical physics will need to possess different analytical and conceptual capabilities than one used in history or literature. This could necessitate the development of specialized agents or AI models flexible enough to adapt to the specificities of each discipline.

In conclusion, the development of AI agents capable of producing multiple-choice exercises from university lecture notes represents considerable potential for improving learning and higher education.

An article by Nadim Mottu, taken from the collective book "Bots and Robots", as part of the AI Shapers 2025 selection.