Autonomous LLM-generated Feedback for Student Exercises in Introductory Software Engineering Courses

TL;DR

This paper presents NAILA, an AI-powered tool that provides autonomous, 24/7 feedback for student exercises in introductory software engineering courses, addressing scalability and personalization challenges.

Contribution

The paper introduces NAILA, a novel system leveraging LLMs for automated student feedback, and evaluates its acceptance, usage, and impact on learning in a large university setting.

Findings

Students find NAILA useful and easy to use.

Frequent engagement with NAILA correlates with improved academic performance.

NAILA effectively provides timely feedback comparable to human instructors.

Abstract

Introductory Software Engineering (SE) courses face rapidly increasing student enrollment numbers, participants with diverse backgrounds and the influence of Generative AI (GenAI) solutions. High teacher-to-student ratios often challenge providing timely, high-quality, and personalized feedback a significant challenge for educators. To address these challenges, we introduce NAILA, a tool that provides 24/7 autonomous feedback for student exercises. Utilizing GenAI in the form of modern LLMs, NAILA processes student solutions provided in open document formats, evaluating them against teacher-defined model solutions through specialized prompt templates. We conducted an empirical study involving 900+ active students at the University of Duisburg-Essen to assess four main research questions investigating (1) the underlying motivations that drive students to either adopt or reject NAILA, (2) user acceptance by measuring perceived usefulness and ease of use alongside subjective learning progress, (3) how often and how consistently students engage with NAILA, and (4) how using NAILA to receive AI feedback impacts on academic performance compared to human feedback.