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Generative AI is increasingly used to enhance educational support in higher education, particularly for distance learning. This paper introduces the Term Paper Recommendation System (TPRS), an AI-powered scaffolding tool designed to assist students in selecting and refining research topics for academic writing. TPRS integrates generative language models with knowledge-based and expert-driven recommendation strategies, dynamically adapting feedback based on the student’s confidence level. The system leverages structured validation, multi-shot prompting with historical supervision data, and semantic similarity for literature recommendations. Deployed within a Bachelor of Arts program in Culture and Social Sciences at FernUniversität in Hagen, TPRS was evaluated using the CRS-Que framework and expert grading of student submissions. Results show statistically significant improvements in topic formulation quality, enhanced engagement, and reduced instructor workload. However, user feedback highlighted the need for improved transparency and control. TPRS offers a novel hybrid architecture that positions AI as a learning scaffold rather than an automation tool. This work contributes to responsible AI integration in higher education by demonstrating how generative systems can support inquiry-based learning while preserving student agency.

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Knowledge Scaffolding Recommendation System for Supervising Term Papers

  • 会议论文

  • 发表日期：

    2026年

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  • Nghia Duong-Trung,
  • Xia Wang,
  • Rahul Rajkumar Bhoyar,
  • Angelin Mary Jose,
  • Silke Elisabeth Wrede,
  • Lars van Rijn,
  • Theresa Panse,
  • Claudia de Witt,
  • Niels Pinkwart

摘要

Generative AI is increasingly used to enhance educational support in higher education, particularly for distance learning. This paper introduces the Term Paper Recommendation System (TPRS), an AI-powered scaffolding tool designed to assist students in selecting and refining research topics for academic writing. TPRS integrates generative language models with knowledge-based and expert-driven recommendation strategies, dynamically adapting feedback based on the student’s confidence level. The system leverages structured validation, multi-shot prompting with historical supervision data, and semantic similarity for literature recommendations. Deployed within a Bachelor of Arts program in Culture and Social Sciences at FernUniversität in Hagen, TPRS was evaluated using the CRS-Que framework and expert grading of student submissions. Results show statistically significant improvements in topic formulation quality, enhanced engagement, and reduced instructor workload. However, user feedback highlighted the need for improved transparency and control. TPRS offers a novel hybrid architecture that positions AI as a learning scaffold rather than an automation tool. This work contributes to responsible AI integration in higher education by demonstrating how generative systems can support inquiry-based learning while preserving student agency.