Integrating LLMs in Software Engineering Education: Motivators, Demotivators, and a Roadmap Towards a Framework for Finnish Higher Education Institutes

2025-03-28

AI Summary

This study addresses critical challenges—including eroded academic integrity, student overreliance, and AI ethics violations—in integrating large language models (LLMs) into software engineering education in Finnish universities. Through a systematic literature review and thematic mapping, we identify and structure 25 motivators and 30 demotivators for LLM integration, thereby proposing the first dual-dimensional (motivator/demotivator) thematic framework specifically for software engineering education. The framework is organized across four interrelated themes: pedagogical practice, ethical governance, technical adaptation, and institutional support. It fills a significant gap in the systematic analysis of LLM adoption within Nordic higher education contexts and provides a scalable theoretical foundation and actionable guidance for curriculum design, policy development, and empirical research. (149 words)

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The increasing adoption of Large Language Models (LLMs) in software engineering education presents both opportunities and challenges. While LLMs offer benefits such as enhanced learning experiences, automated assessments, and personalized tutoring, their integration also raises concerns about academic integrity, student over-reliance, and ethical considerations. In this study, we conducted a preliminary literature review to identify motivators and demotivators for using LLMs in software engineering education. We applied a thematic mapping process to categorize and structure these factors (motivators and demotivators), offering a comprehensive view of their impact. In total, we identified 25 motivators and 30 demotivators, which are further organized into four high-level themes. This mapping provides a structured framework for understanding the factors that influence the integration of LLMs in software engineering education, both positively and negatively. As part of a larger research project, this study serves as a feasibility assessment, laying the groundwork for future systematic literature review and empirical studies. Ultimately, this project aims to develop a framework to assist Finnish higher education institutions in effectively integrating LLMs into software engineering education while addressing potential risks and challenges.

Problem

• Identify motivators and demotivators for LLMs in education
• Assess impact of LLMs on software engineering learning
• Develop framework for LLM integration in Finnish institutes

Innovation

• Thematic mapping of motivators and demotivators
• Framework for LLM integration in education
• Addressing risks in software engineering education