Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications
Project Overview
The document examines the incorporation of generative AI (GenAI) in Software Engineering (SE) education, emphasizing the importance of responsible usage to prevent overreliance and promote critical thinking among students. It introduces a causal model designed to assist educators in effectively integrating GenAI into their curricula. Two empirical applications are highlighted: the redesign of lab assignments in a Software Testing course at Queen’s University Belfast and the establishment of a new SE Bachelor’s program at Azerbaijan Technical University. These initiatives aim to foster reflective engagement with GenAI tools while ensuring that students maintain vital cognitive skills. The outcomes suggest that when GenAI is thoughtfully integrated, it can enhance learning experiences and support the development of practical skills, preparing students for future challenges in the field without diminishing their ability to think independently and critically.
Key Applications
Causal model and curriculum design for responsible GenAI use in Software Engineering education
Context: Final-year Software Testing course at Queen's University Belfast and newly developed Software Engineering BSc program at Azerbaijan Technical University. Both contexts aim to enhance AI literacy and responsible use of GenAI in software engineering education.
Implementation: Revised lab assignments and curriculum to include GenAI usage guidelines, validation tasks, reflective prompts, and embedded GenAI-related competencies across the program. This approach integrates GenAI literacy with foundational competencies and emphasizes critical engagement.
Outcomes: Increased critical engagement with GenAI, reduced passive reliance on AI, improved awareness of validation practices, and longitudinal scaffolding of AI literacy aligned with program goals.
Challenges: Student overreliance on GenAI, difficulty in ensuring deep understanding of concepts, and the challenge of integrating GenAI literacy without compromising foundational competencies.
Implementation Barriers
Educational barrier
Students may skip cognitive effort by relying on GenAI, negatively impacting learning.
Proposed Solutions: Implement GenAI usage guidelines and validation tasks in assignments.
Ethical barrier
Concerns over academic integrity and potential misuse of GenAI tools.
Proposed Solutions: Establish clear policies and guidelines for acceptable use of GenAI in assessments.
Technological barrier
GenAI tools may produce inaccurate or misleading outputs.
Proposed Solutions: Incorporate training on AI validation and critical assessment of GenAI outputs.
Project Team
Vahid Garousi
Researcher
Zafar Jafarov
Researcher
Aytan Movsumova
Researcher
Atif Namazov
Researcher
Huseyn Mirzayev
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Vahid Garousi, Zafar Jafarov, Aytan Movsumova, Atif Namazov, Huseyn Mirzayev
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai