# Analyzing Feedback Mechanisms in AI-Generated MCQs: Insights into Readability, Lexical Properties, and Levels of Challenge

arXiv: https://arxiv.org/abs/2504.21013 · HTML: https://mlsanity.com/paper/2504.21013/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 19 Apr 2025.

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

- [Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation](https://mlsanity.com/paper/2505.00339/) (2025)
- [Lightweight Prompt Engineering for Cognitive Alignment in Educational AI: A OneClickQuiz Case Study](https://mlsanity.com/paper/2510.03374/) (2025)
- [Reviewing the Reviewer: LLM-Assisted Reviewer Feedback Generation for Guideline Compliance](https://mlsanity.com/paper/2602.10118/) (2026)
- [Explainability and Certification of AI-Generated Educational Assessments](https://mlsanity.com/paper/2604.09622/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI)-generated feedback in educational settings has garnered considerable attention due to its potential to enhance learning outcomes. However, a comprehensive understanding of the linguistic characteristics of AI-generated feedback, including readability, lexical richness, and adaptability across varying challenge levels, remains limited. This study delves into the linguistic and structural attributes of feedback generated by Google's Gemini 1.5-flash text model for computer science multiple-choice questions (MCQs). A dataset of over 1,200 MCQs was analyzed, considering three difficulty levels (easy, medium, hard) and three feedback tones (supportive, neutral, challenging). Key linguistic metrics, such as length, readability scores (Flesch-Kincaid Grade Level), vocabulary richness, and lexical density, were computed and examined. A fine-tuned RoBERTa-based multi-task learning (MTL) model was trained to predict these linguistic properties, achieving a Mean Absolute Error (MAE) of 2.0 for readability and 0.03 for vocabulary richness. The findings reveal significant interaction effects between feedback tone and question difficulty, demonstrating the dynamic adaptation of AI-generated feedback within diverse educational contexts. These insights contribute to the development of more personalized and effective AI-driven feedback mechanisms, highlighting the potential for improved learning outcomes while underscoring the importance of ethical considerations in their design and deployment.