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Human-in-the-Loop Systems for Adaptive Learning Using Generative AI

Authors
Bhavishya Tarun,
Haoze Du,
Dinesh Kannan,
Dr. Edward F. Gehringer
Date
Publisher
arXiv
Link
http://arxiv.org/pdf/2508.11062v1
A Human-in-the-Loop (HITL) approach leverages generative AI to enhance personalized learning by directly integrating student feedback into AI-generated solutions. Students critique and modify AI responses using predefined feedback tags, fostering deeper engagement and understanding. This empowers students to actively shape their learning, with AI serving as an adaptive partner. The system uses a tagging technique and prompt engineering to personalize content, informing a Retrieval-Augmented Generation (RAG) system to retrieve relevant educational material and adjust explanations in real time. This builds on existing research in adaptive learning, demonstrating how student-driven feedback loops can modify AI-generated responses for improved student retention and engagement, particularly in STEM education. Preliminary findings from a study with STEM students indicate improved learning outcomes and confidence compared to traditional AI tools. This work highlights AI's potential to create dynamic, feedback-driven, and personalized learning environments through iterative refinement.
Study at a glance
  • Question: Can allowing students to guide and modify AI responses improve their learning in STEM subjects?
  • Setting and data: STEM students.
  • Method: Not stated in the abstract.
Why it matters

Building on adaptive learning research, integrating direct student feedback into AI-generated solutions empowers students to actively shape their learning. Enabling students to critique and modify AI responses fosters deeper engagement and understanding, particularly in STEM education.

Key findings
  • Among STEM students, preliminary findings indicate that interactive AI improved learning outcomes and student confidence compared to traditional AI tools.
  • Student-driven feedback loops that actively modify AI responses foster deeper student understanding, retention, and engagement.
  • Directly integrating student feedback enables generative AI to retrieve relevant educational materials and adjust explanations in real time.

Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.

What is the application?
Teaching – Instructional Materials,
Learning – Student Support
Who is the user?
Student
Who age?
Post-Secondary
Why use AI?
Outcomes – Other Academic,
Outcomes – Differentiation
Study design
Technical – Computational,
Quantitative – Others
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