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Open research questions in Intelligent Tutoring Systems and Adaptive Learning

457 unresolved questions extracted from the limitations and future-work sections of 2,385 Intelligent Tutoring Systems and Adaptive Learning papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • limited generalization ability, - small number of clips for external evaluation, - evaluation is conducted at the clip level, - inherent trade-offs between convergence stability and predictive behavior

    A customized AI-based machine learning model for evaluating participants’ activities under a workshop setting · 2026 · DOI

  • further analysis of model behavior under data-limited and cross-domain conditions, - examination of the model's performance with larger datasets, - investigation of the model's ability to generalize to other domains

    A customized AI-based machine learning model for evaluating participants’ activities under a workshop setting · 2026 · DOI

  • investigating the application of temporal features in other educational contexts, - exploring the use of HMCI in different learning environments, - examining the impact of feedback frequency and complexity on learner behaviour

    Tracing learner responsiveness through temporal interaction patterns · 2026 · DOI

  • the study used a cross-sectional design, - the sample was limited to Jordanian secondary school teachers, - the study did not investigate the long-term effects of using C-SARTS

    Cognitive, Spiritual, and Algorithmic Responsiveness in Teaching Scale (C-SARTS): Exploring Teachers’ Cognitive, Ethical, and Spiritual Responsiveness in AI–Supported Secondary Education in Jordan · 2026 · DOI

  • investigating the long-term effects of using C-SARTS, - exploring the use of C-SARTS in other educational settings, - examining the relationship between C-SARTS and student outcomes

    Cognitive, Spiritual, and Algorithmic Responsiveness in Teaching Scale (C-SARTS): Exploring Teachers’ Cognitive, Ethical, and Spiritual Responsiveness in AI–Supported Secondary Education in Jordan · 2026 · DOI

  • further investigation into the effects of GenAI tools on students' CT - examination of the impact of GenAI tools on specific CT sub-skills - exploration of the role of participation method and sample size in moderating the effects of GenAI tools on students' CT

    Effects of Generative AI Tools On Students’ Critical Thinking: A Three-Level Meta-Analysis · 2026 · DOI

  • The inconsistent empirical evidence on the impact of GenAI tools on students' CT. The lack of a comprehensive understanding of the effect of GenAI tools on students' CT. The need to identify specific study characteristics that moderate the impact of GenAI tools on students' CT.

    Effects of Generative AI Tools On Students’ Critical Thinking: A Three-Level Meta-Analysis · 2026 · DOI

  • Bridging the human side with the AI side reveals new empirical and practical questions for educational psychology scholars

    From AI as a Relational Breaker to a Relational Broker: Comment on Bauer et al. (2025) · 2026 · DOI

  • The gap in current research is the lack of consideration of human relational dynamics in AI-facilitated learning. The authors identify a need to bridge the 'human side' with the 'AI side' in educational psychology. The gap is the lack of understanding of how relational conditions impact the effectiveness of AI in education.

    From AI as a Relational Breaker to a Relational Broker: Comment on Bauer et al. (2025) · 2026 · DOI

  • The lack of a clear connection between AI model training and educational psychology. The need for a set of validity checks to distinguish robust competence from support dependence, proxy optimization, and rater-specific compliance.

    Five Educational-Psychology Lenses for Training and Evaluating AI Models · 2026 · DOI

  • Current AI applications are limited by general-purpose datasets. There is a need for models that can capture the pedagogical meaning of student actions in authentic educational contexts.

    Leveraging Multimodal Large Language Models to Analyse Student Exploration Behaviours in Educational Game Environments · 2026 · DOI

  • The need for a framework that provides students with transparent genAI guidance. The lack of a validated instrument for measuring student perceptions of genAI transparency. The need for a faculty development model that guides instructors in creating transparent assignment-level guidance regarding genAI use.

    Making Generative AI Expectations Visible: Extending the TILT Framework for Transparent Assignment Design · 2026 · DOI

  • sustained accumulation of research rather than editorial intervention, - examination of whether AI systems that adapt to observed performance interact with the masking that defines twice-exceptionality, - research on the categories of AI most likely to shape 2e learners' educational trajectories at scale

    Artificial intelligence and twice-exceptional (2e) learners in educational settings: a scoping review of an emerging evidence base (2020–2026) · 2026 · DOI

  • the current evidence base on AI for 2e learners is small and emerging - there is a need for more research on the intersection of AI and twice-exceptionality - the literature on AI for 2e learners is limited by small sample sizes and uneven outcome measurement

    Artificial intelligence and twice-exceptional (2e) learners in educational settings: a scoping review of an emerging evidence base (2020–2026) · 2026 · DOI

  • feature extraction and interpreting results for practical applications are persistent challenges, - eye-tracking requires specialized equipment and controlled environments, - keystroke data often lacks contextual information about task navigation or resource use

    The Application of Machine Learning to Educational Process Data Analysis: A Systematic Review · 2025 · DOI

  • enhancing personalized learning, - improving assessment accuracy, - promoting test fairness, - sustained and growing interest in the application of process data analysis in educational research

    The Application of Machine Learning to Educational Process Data Analysis: A Systematic Review · 2025 · DOI

  • further validation of the TILTai Scale, - exploration of the effects of the TILTai Framework on student perceptions of genAI's role on assignments

    Making Generative AI Expectations Visible: Extending the TILT Framework for Transparent Assignment Design · 2026 · DOI

  • Many of the questions that matter most for practice, particularly those concerning learner development, use in authentic settings, and institutional responsibility, remain insufficiently examined.

    Large language models for teaching and learning in higher education: opportunities, challenges, and future directions · 2026 · DOI

  • While user control is widely assumed to improve user experience, the effects of different levels of control in ERSs remain underexplored.

    Investigating the Effects of Different Levels of User Control in an Interactive Educational Recommender System · 2026 · DOI

  • Had a British monk adopted such a strategy ten centuries ago, English speaking children now would not be limited to the 26 symbol Latin alphabet, and would not have to face so many irregular sound- symbol relationships when learning to decode.

    Generative strategies and teaching for generalization · 1987 · DOI

  • There is a need to examine the applications of AI in education to enhance the learning process. The study identifies a gap in the literature regarding the effective integration of AI-supported applications in science education.

    Artificial Intelligence-supported applications used in Science Education · 2026 · DOI

  • Using the student's academic performance, skills, and internship data to predict the chances of a student getting placed. Suggesting what skills students need to improve for better career opportunities.

    CSE Mentor: an ML-Driven Virtual Mentor for Department · 2026 · DOI

  • Traditional mentorship systems have limitations, such as limited one-on-one interactions and generalized guidance. There is a need for a personalized and data-driven approach to academic mentorship.

    CSE Mentor: an ML-Driven Virtual Mentor for Department · 2026 · DOI

  • Prior AES systems have limitations in terms of interpretability and learner-centered support. Prior studies have encountered challenges in effectively balancing shared and trait-specific representations.

    CNN–transformer-based trait-aware scoring and student-aware feedback generation for English writing · 2026 · DOI

  • Future work will focus on few-shot learner modeling to mitigate data sparsity, the incorporation of lightweight adapter modules to improve scal- ability, and the extension of TAS-AF to multilingual and cross-domain writing tasks to broaden its applicability in diverse educational contexts.

    CNN–transformer-based trait-aware scoring and student-aware feedback generation for English writing · 2026 · DOI

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457 open questions have been extracted from the limitations and future-work passages of 2,385 Intelligent Tutoring Systems and Adaptive Learning papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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