Generative AI’s Ethical and Societal Impacts in Higher Computing Education: a Systematic Review

Generative AI now presents significant ethical and societal challenges, particularly within education, and demands careful consideration of issues like equity, academic integrity, and bias. Janice Mak, Joyce Nakatumba-Nabende, Tony Clear, and colleagues address these concerns by systematically investigating the rapidly evolving landscape of generative AI and its impact on higher computing education. Their work synthesises existing research and evaluates international university policies to create the Ethical and Societal Impacts-Framework, a valuable resource for educators, computing professionals, and policymakers. This framework guides decision-making as institutions navigate the integration of generative AI, ensuring responsible and ethical implementation within computing education and beyond.

AI and Programming Education Research Trends

This compilation of research explores the evolving landscape of Artificial Intelligence in Computer Science Education, specifically focusing on programming. The collection allows for several analytical approaches, including identifying overarching trends, extracting specific themes like automated assessment or student perceptions of tools, and tracking the evolution of research over time. Researchers can organize the data by keywords, categorize it into broader research areas such as student support or AI-assisted coding, or identify potential redundancies. To begin, it is important to define the primary research goal and desired format for presenting the findings, such as a summary or categorized list.

GenAI Impact on Higher Computing Education

This research team conducted a systematic investigation into the ethical and societal impacts of Generative AI (GenAI) within higher computing education. This evaluation identified common themes, discrepancies, and emerging best practices in governing GenAI, specifically regarding academic integrity, bias, data provenance, and equitable access. This framework serves as a practical tool for educators, computing professionals, and policymakers, providing guidance for responsible integration of GenAI and informed decision-making.

GenAI Ethics in Higher Computing Education

This work presents a comprehensive analysis of generative AI (GenAI) within higher computing education, focusing on its ethical and societal impacts. Researchers identified 94 review studies, ultimately focusing on six specifically addressing GenAI in higher education, with only three discussing ethical issues, reflecting a broader need for ethical considerations within the computing discipline. A systematic literature review analyzed 71 papers, revealing a strong emphasis on the capabilities of GenAI tools and framing potential ethical issues within the context of the ACM Code of Ethics. Another review examined 21 papers, finding that teachers commonly use LLM-based code generation models for generating assignments and evaluating student work, while students utilize them as virtual tutors, also identifying risks to academic integrity and potential learning issues due to errors or over-reliance on the models. Further analysis of seven reviews revealed a focus on the practical applications of GenAI in computing classrooms, triangulated with surveys and interviews to understand current practices. The findings demonstrate a complex landscape where issues of academic integrity, equity, and power dynamics are significantly affected by the increasing use of generative AI tools. The study highlights a growing disparity in student access to and usage of generative AI, mirroring earlier digital divides. However, the research also reveals opportunities to leverage these tools to create more inclusive learning materials, including support for multiple languages and culturally relevant content. Furthermore, the team identified a shift in power dynamics between educators and students, as instructors increasingly rely on automated detection tools, which can raise concerns about accuracy and fairness.