# SlideItRight: Using AI to Find Relevant Slides and Provide Feedback for Open-Ended Questions

arXiv: https://arxiv.org/abs/2505.04584 · HTML: https://mlsanity.com/paper/2505.04584/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2025.

## Built on by (most cited first, 4 of 4)

- [Automatic Large Language Models Creation of Interactive Learning Lessons](https://mlsanity.com/paper/2506.17356/) (2025)
- [Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook](https://mlsanity.com/paper/2509.16780/) (2025)
- [LLM-based Multimodal Feedback Produces Equivalent Learning and Better Student Perceptions than Educator Feedback](https://mlsanity.com/paper/2601.15280/) (2026)
- [Chat-Based Support Alone May Not Be Enough: Comparing Conversational and Embedded LLM Feedback for Mathematical Proof Learning](https://mlsanity.com/paper/2602.18807/) (2026)

## Abstract (arXiv)

Feedback is important in supporting student learning. While various automated feedback systems have been implemented to make the feedback scalable, many existing solutions only focus on generating text-based feedback. As is indicated in the multimedia learning principle, learning with more modalities could help utilize more separate channels, reduce the cognitive load and facilitate students' learning. Hence, it is important to explore the potential of Artificial Intelligence (AI) in feedback generation from and to different modalities. Our study leverages Large Language Models (LLMs) for textual feedback with the supplementary guidance from other modality - relevant lecture slide retrieved from the slides hub. Through an online crowdsourcing study (N=91), this study investigates learning gains and student perceptions using a 2x2 design (i.e., human feedback vs. AI feedback and with vs. without relevant slide), evaluating the clarity, engagement, perceived effectiveness, and reliability) of AI-facilitated multimodal feedback. We observed significant pre-to-post learning gains across all conditions. However, the differences in these gains were not statistically significant between conditions. The post-survey revealed that students found the slide feedback helpful in their learning process, though they reported difficulty in understanding it. Regarding the AI-generated open-ended feedback, students considered it personalized and relevant to their responses, but they expressed lower trust in the AI feedback compared to human-generated feedback.