Prediction Model of Motivators and Demotivators of Integrating Large Language Models in Software Engineering Education: An Empirical Study

2026-05-10

AI Summary

This study addresses the lack of a systematic, evidence-based, and cost-effective framework for integrating large language models (LLMs) into software engineering education. Drawing on survey data from 126 multinational stakeholders, it employs Naïve Bayes and logistic regression to identify key drivers—such as programming assistance and personalized learning—and barriers—including plagiarism risks and cognitive offloading. The work further introduces a novel decision-support framework that uniquely integrates probabilistic prediction models with genetic algorithms to balance pedagogical impact against implementation costs. This framework advocates a phased integration strategy that prioritizes ethical safeguards and academic integrity mechanisms under budget constraints, embodying a “governance-first” optimization pathway to achieve multi-level alignment between educational effectiveness and resource efficiency.

Abstract

Context: Large Language Models (LLMs) are increasingly influencing software engineering practice and education. While prior studies examine their technical performance and classroom use, limited research provides cost-aware and empirically grounded models for systematic institutional integration. Objective: This study develops and validates a prediction model to identify cost-efficient strategies for integrating LLMs into software engineering education using motivating and demotivating factors. Method: Based on our previously developed literature survey taxonomies [1], we operationalized 19 validated factors (9 motivators and 10 demotivators) into a structured survey completed by 126 stakeholders from multiple countries. Likert-scale responses were encoded and used to train probabilistic models (Naive Bayes and Logistic Regression) to estimate the likelihood of high LLM familiarity. The probability estimates were integrated into a Genetic Algorithm (GA)-based optimization framework to model trade-offs between predicted familiarity and implementation cost at global and category levels. Results: Respondents perceived strong benefits in Programming Assistance and Debugging Support and Personalized and Adaptive Learning. Major concerns included Plagiarism and Intellectual Property Concerns, Over-Reliance on AI in Learning, and Reduced Critical Thinking and Problem Solving. Optimization results indicate that governance-related mechanisms, particularly integrity and ethical safeguards, should be prioritized under cost constraints. Conclusions: The study introduces an optimization-informed decision support framework linking stakeholder perceptions with probabilistic modeling and cost-effort analysis. The model supports staged and cost-aware LLM integration grounded in governance stability and pedagogically meaningful development.

Problem

• Large Language Models
• Software Engineering Education
• Motivators
• Demotivators
• Cost-aware Integration

Innovation

• Large Language Models
• Prediction Model
• Genetic Algorithm Optimization
• Cost-Aware Integration
• Software Engineering Education