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Towards a personalized AI assistant to learn machine learning

Nature Machine Intelligence volume 6, pages 1413–1414 (2024)Cite this article

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The introduction and rapid public adoption of generative AI tools such as OpenAI’s ChatGPT in late 2022 dramatically affected the educational landscape. Many students — perhaps the majority — now routinely use ChatGPT and similar models for coursework. In the classes we taught at New York University (NYU), over 90% of undergraduate and master’s students reported using large language models (LLMs) in 2023, rising to over 95% in 2024. The widespread use of LLMs, for example, in personal assistants, promises to be a tremendous boon for learning. However, they could also prove detrimental.

One concern is that these AI tools could supplant learning. For instance, a generative AI tool could write code or essays for a student, instead of the student learning how to write code or essays themself. If generative AI tools reduce diligence by decreasing the time students spend with learning materials, the resulting decrements in learning could be substantial1. Another critical problem with using LLM-based tools for learning is that they were trained on vast, sometimes unreliable text corpora and respond in a probabilistic manner. This makes ‘hallucinations’ — contradictory, inaccurate or false outputs — inevitable2. As students often accept these outputs at face value, probably influenced by the confident tone of LLMs, hallucinations pose a risk to the integrity of learning itself.

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Fig. 1: Retrieval-augmented generation workflow in an education setting.

Code availability

Code is available at https://github.com/Ibrahimsheikh02/PlutarchOS. A fully deployed version can be accessed at https://www.plutarch.us/.

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Authors and Affiliations

  1. Center for Data Science, New York University, New York, NY, USA

    Pascal Wallisch

  2. College of Computing and Information Science, Cornell University, Ithaca, NY, USA

    Ibrahim Sheikh

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  1. Pascal Wallisch
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  2. Ibrahim Sheikh
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Correspondence to Pascal Wallisch.

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The authors declare no competing interests.

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Wallisch, P., Sheikh, I. Towards a personalized AI assistant to learn machine learning. Nat Mach Intell 6, 1413–1414 (2024). https://doi.org/10.1038/s42256-024-00953-0

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