On the Role of Domain Experts in Creating Effective Tutoring Systems

• URL: http://arxiv.org/abs/2510.01432v1
• Date: Wed, 01 Oct 2025 20:12:57 GMT
• Title: On the Role of Domain Experts in Creating Effective Tutoring Systems
• Authors: Sarath Sreedharan, Kelsey Sikes, Nathaniel Blanchard, Lisa Mason, Nikhil Krishnaswamy, Jill Zarestky,
• Abstract summary: We will look at how one could use explainable AI (XAI) techniques to automatically create lessons.<n>Secondly, we will see how an expert specified curriculum for learning a target concept can help develop adaptive tutoring systems.
• Score: 20.357280524494023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The role that highly curated knowledge, provided by domain experts, could play in creating effective tutoring systems is often overlooked within the AI for education community. In this paper, we highlight this topic by discussing two ways such highly curated expert knowledge could help in creating novel educational systems. First, we will look at how one could use explainable AI (XAI) techniques to automatically create lessons. Most existing XAI methods are primarily aimed at debugging AI systems. However, we will discuss how one could use expert specified rules about solving specific problems along with novel XAI techniques to automatically generate lessons that could be provided to learners. Secondly, we will see how an expert specified curriculum for learning a target concept can help develop adaptive tutoring systems, that can not only provide a better learning experience, but could also allow us to use more efficient algorithms to create these systems. Finally, we will highlight the importance of such methods using a case study of creating a tutoring system for pollinator identification, where such knowledge could easily be elicited from experts.

Related papers

• AI-Powered Math Tutoring: Platform for Personalized and Adaptive Education [0.0]
  We introduce a novel multi-agent AI tutoring platform that combines adaptive and personalized feedback, structured course generation, and textbook knowledge retrieval.<n>This system allows students to learn new topics while identifying and targeting their weaknesses, revise for exams effectively, and practice on an unlimited number of personalized exercises.
  arXiv  Detail & Related papers  (2025-07-14T20:35:16Z)
• Integrating Cognitive AI with Generative Models for Enhanced Question Answering in Skill-based Learning [3.187381965457262]
  This paper proposes a novel approach that merges Cognitive AI and Generative AI to address these challenges. We employ a structured knowledge representation, the TMK (Task-Method-Knowledge) model, to encode skills taught in an online Knowledge-based AI course.
  arXiv  Detail & Related papers  (2024-07-28T04:21:22Z)
• Transferring Domain Knowledge with (X)AI-Based Learning Systems [3.0059120458540383]
  Explainable artificial intelligence (XAI) has conventionally been used to make black-box artificial intelligence systems interpretable. An (X)AI system is trained on experts' past decisions and is then employed to teach novices by providing examples and explanations. We show that (X)AI-based learning systems are able to induce learning in novices and that their cognitive styles moderate learning.
  arXiv  Detail & Related papers  (2024-06-03T13:56:30Z)
• Instructors as Innovators: A future-focused approach to new AI learning opportunities, with prompts [0.0]
  This paper explores how instructors can leverage generative AI to create personalized learning experiences for students. We present a range of AI-based exercises that enable novel forms of practice and application including simulations, mentoring, coaching, and co-creation.
  arXiv  Detail & Related papers  (2024-04-23T04:01:38Z)
• Toward enriched Cognitive Learning with XAI [44.99833362998488]
  We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools. The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
  arXiv  Detail & Related papers  (2023-12-19T16:13:47Z)
• Towards Human Cognition Level-based Experiment Design for Counterfactual Explanations (XAI) [68.8204255655161]
  The emphasis of XAI research appears to have turned to a more pragmatic explanation approach for better understanding. An extensive area where cognitive science research may substantially influence XAI advancements is evaluating user knowledge and feedback. We propose a framework to experiment with generating and evaluating the explanations on the grounds of different cognitive levels of understanding.
  arXiv  Detail & Related papers  (2022-10-31T19:20:22Z)
• Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach [40.06500618820166]
  This paper presents an approach to deriving a learner model directly from an assessment rubric. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills.
  arXiv  Detail & Related papers  (2022-09-07T10:09:12Z)
• Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
  We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-03-19T03:22:57Z)
• Decision Rule Elicitation for Domain Adaptation [93.02675868486932]
  Human-in-the-loop machine learning is widely used in artificial intelligence (AI) to elicit labels from experts. In this work, we allow experts to additionally produce decision rules describing their decision-making. We show that decision rule elicitation improves domain adaptation of the algorithm and helps to propagate expert's knowledge to the AI model.
  arXiv  Detail & Related papers  (2021-02-23T08:07:22Z)
• Distributed and Democratized Learning: Philosophy and Research Challenges [80.39805582015133]
  We propose a novel design philosophy called democratized learning (Dem-AI) Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently. We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
  arXiv  Detail & Related papers  (2020-03-18T08:45:10Z)
• Explainable Active Learning (XAL): An Empirical Study of How Local Explanations Impact Annotator Experience [76.9910678786031]
  We propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an Active Learning setting. Our study shows benefits of AI explanation as interfaces for machine teaching--supporting trust calibration and enabling rich forms of teaching feedback, and potential drawbacks--anchoring effect with the model judgment and cognitive workload.
  arXiv  Detail & Related papers  (2020-01-24T22:52:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.