# Bridging LMS and generative AI: dynamic course content integration (DCCI) for enhancing student satisfaction and engagement via the ask ME assistant

arXiv: https://arxiv.org/abs/2504.03966 · HTML: https://mlsanity.com/paper/2504.03966/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 95 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [Hallucinations in Large Multilingual Translation Models](https://mlsanity.com/paper/2303.16104/) (2023)
- [Personalization of Large Language Models: A Survey](https://mlsanity.com/paper/2411.00027/) (2024)
- [Parallel Context Windows for Large Language Models](https://mlsanity.com/paper/2212.10947/) (2022)
- [Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring](https://mlsanity.com/paper/2411.16337/) (2024)
- [Artificial Intelligence-Enabled Intelligent Assistant for Personalized and Adaptive Learning in Higher Education](https://mlsanity.com/paper/2309.10892/) (2023)
- [Harnessing LLMs in Curricular Design: Using GPT-4 to Support Authoring of Learning Objectives](https://mlsanity.com/paper/2306.17459/) (2023)
- [Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionals](https://mlsanity.com/paper/2403.14691/) (2024)
- [Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education](https://mlsanity.com/paper/2302.09294/) (2023)
- [Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospects](https://mlsanity.com/paper/2305.03433/) (2023)
- [Automating Autograding: Large Language Models as Test Suite Generators for Introductory Programming](https://mlsanity.com/paper/2411.09261/) (2024)
- [CourseAssist: Pedagogically Appropriate AI Tutor for Computer Science Education](https://mlsanity.com/paper/2407.10246/) (2024)
- [Scaling Evidence-based Instructional Design Expertise through Large Language Models](https://mlsanity.com/paper/2306.01006/) (2023)
- [Integrating A.I. in Higher Education: Protocol for a Pilot Study with 'SAMCares: An Adaptive Learning Hub'](https://mlsanity.com/paper/2405.00330/) (2024)
- [Revolutionizing Undergraduate Learning: CourseGPT and Its Generative AI Advancements](https://mlsanity.com/paper/2407.18310/) (2024)

## Abstract (arXiv)

Integration of Large Language Models (LLMs) with Learning Management Systems (LMSs) can enhance task automation and accessibility in education. However, hallucination where LLMs generate inaccurate or misleading information remains a challenge. This study introduces the Dynamic Course Content Integration (DCCI) mechanism, which dynamically retrieves course content from Canvas LMS and structures it within an LLM's context window via prompt engineering, enabling the LLM-powered assistant, Ask ME, to deliver context-aware, curriculum-aligned responses while mitigating hallucinations. A mixed-methods pilot study grounded in Self-Determination Theory (autonomy, competence) and the Technology Acceptance Model (perceived usefulness, ease of use) evaluated DCCI's effectiveness with 120 first-year programming students at E"otv"os Lor'and University. The course focused on foundational programming patterns in C#, including writing program specifications. We analyzed 14,746 logged interactions and a post-course survey completed by 101 students. User satisfaction was measured via a 5-point Likert scale (turn-level ratings), while the survey assessed usability, engagement, and ethical concerns. Results indicated high satisfaction (mean 4.65/5) and strong recognition of Ask ME's ability to provide timely, contextually relevant answers to administrative and course-related queries. 78.06% agreed that Ask ME's Canvas integration reduced platform switching, improving usability, engagement, comprehension, and topic exploration. Many students reported reduced hesitation to ask questions and increased motivation for self-directed learning, though concerns about over-reliance on AI and reduced student-teacher interaction emerged. This study demonstrates that DCCI enhances LLM reliability, student satisfaction, and engagement in AI-driven educational automation, while highlighting the importance of balancing