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Toward In-Context Teaching: Adapting Examples to Students' Misconceptions (2405.04495v1)

Published 7 May 2024 in cs.CL, cs.AI, and cs.LG

Abstract: When a teacher provides examples for a student to study, these examples must be informative, enabling a student to progress from their current state toward a target concept or skill. Good teachers must therefore simultaneously infer what students already know and adapt their teaching to students' changing state of knowledge. There is increasing interest in using computational models, particularly LLMs, as pedagogical tools. As students, LLMs in particular have shown a remarkable ability to adapt to new tasks given small numbers of examples. But how effectively can these models adapt as teachers to students of different types? To study this question, we introduce a suite of models and evaluation methods we call AdapT. AdapT has two components: (1) a collection of simulated Bayesian student models that can be used for evaluation of automated teaching methods; (2) a platform for evaluation with human students, to characterize the real-world effectiveness of these methods. We additionally introduce (3) AToM, a new probabilistic model for adaptive teaching that jointly infers students' past beliefs and optimizes for the correctness of future beliefs. In evaluations of simulated students across three learning domains (fraction arithmetic, English morphology, function learning), AToM systematically outperforms LLM-based and standard Bayesian teaching models. In human experiments, both AToM and LLMs outperform non-adaptive random example selection. Our results highlight both the difficulty of the adaptive teaching task and the potential of learned adaptive models for solving it.

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Authors (2)
  1. Alexis Ross (13 papers)
  2. Jacob Andreas (116 papers)
Citations (2)
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Summary

  • The paper introduces the AToM model within the AdapT framework, showing its ability to infer and adapt to student misconceptions in real time.
  • Simulated experiments demonstrate that AToM outperforms traditional methods and advanced LLMs by closely matching optimal student responses.
  • Real-world evaluations reveal that both AToM and GPT-4 offer clear advantages over non-adaptive strategies, paving the way for personalized AI-driven education.

Understanding Adaptive Teaching Models through AToM and AdapT Framework

What is AToM and the AdapT Framework?

Understanding how to effectively teach students who come with different backgrounds and misconceptions about a subject is crucial. The AdapT framework along with the teaching model called AToM (Adaptive Teaching toward Misconceptions), tackles this issue head-on.

  • AdapT Framework: This is a split into two components:
    • Simulated student models for various concepts like fraction arithmetic and verb conjugation where students have pre-set misconceptions.
    • Real-world evaluations with human subjects learning concepts through interactions.

    Through this framework, we can measure how different teaching strategies cater to varying student misconceptions using both real and simulated environments.

  • AToM Model: AToM is a probabilistic model designed within the AdapT framework,

    • It infers a student’s misconceptions in real-time and adapits its teaching strategy to correct those misconceptions efficiently.

Results from Simulated and Human Experiments

Simulated assessments showed that AToM could consistently outperform traditional methods and even sophisticated LLMs like GPT-4 when teaching simulated students. However, in real-world tests, both AToM and GPT-4 outperformed non-adaptive strategies.

  • Simulated Student Performance: AToM achieved close to optimal results and showcased the importance of adapting teaching strategies based on inferred student misconceptions.
  • Human Subject Tests: While adapting to the student's learning style still showed benefits, the performance differences between AToM and GPT-4 weren’t as pronounced, suggesting that simple adaptive strategies might be very effective.

Practical Implications

The ability of models like AToM to infer and adapt to individual student’s misconceptions has profound implications:

  • Educational Technologies: Tools can be developed to assist human teachers or to provide robust automated teaching aids, especially in standardized learning segments like mathematics.
  • Personalized Learning: As AI becomes more integrated into educational methodologies, systems like AToM can pave the way for highly personalized learning experiences, optimizing student-teacher interactions to cater specifically to an individual’s learning needs.

Future Developments in AI and Education

There's considerable potential for improving these models by:

  • Combining Learning Strategies: Integrating the strategic adaptivity of AToM with the broad capabilities of LLMs like GPT-4 could lead to even more effective teaching tools.
  • Richer Interactions: Future models could account for back-and-forth interactions where students ask questions and teachers provide explanations, making the teaching process more dynamic.

Conclusion

The exploration of adaptive teaching through the AdapT framework and the AToM model showcases a promising direction in education technology. As we continue to refine these models and integrate them with other AI technologies, the dream of highly personalized and efficient learning experiences becomes increasingly achievable.

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