General guidelines on how to assess Informatics (or Higher Education) students: AI-centric approaches

GENERAL GUIDELINES ON HOW TO ASSESS INFORMATICS (OR HIGHER EDUCATION) STUDENTS: AI-CENTRIC APPROACHES Extended outcomes from ECSS 2024's AI in Informatics Education and Professional Practice Workshop General guidelines on how to assess Informatics (or Higher Education) students: AI-centric approaches Based on discussions at the AI in Informatics Education and Professional Practice Workshop in collaboration with the National Informatics Associations at ECSS 2024 Introduction This document summarises the outcomes from the National Informatics Association (NIA) workshop at European Informatics Leaders Summit (ECSS) 2024, which focused on AI in Informatics Education and Professional Practice. It builds on a previous recommendation document that was released by Informatics Europe [1], addressing the impact of generative AI tools like ChatGPT and GitHub Copilot on Informatics education. In this document, the main focus is on the need for guidelines and best practices on how to assess Informatics (and, in general, higher education) students by means of, e.g. oral exams and written work. The key recommendations are as follows: 1. Reevaluate/update learning outcomes/goals AI tools necessitate a reconsideration of the fundamental goals of Informatics education at large. Likewise, at the course and student work level, learning outcomes need to be kept up-to-date and aligned with current developments in AI tools. This includes an emphasis on broader competencies and considerations such as ethical, legal, and societal aspects of software technology, as well as requirement specification, validation, and development methodologies. Additionally, students should develop the ability to critically assess AI-generated content for accuracy and security, as well as biases and misinformation, understand AI ethics and responsible AI development beyond just usage, and acquire skills in human-AI collaboration, optimising the synergy between AI and human decision-making. 2. Curriculum Revisions Universities should adapt their curricula to incorporate AI tools, aligning with industry needs and future employer expectations, while at the same time further developing students’ problem-solving skills. There should be an immediate focus on integrating AI tools. Here, strategies are needed on how to prevent AI misuse in coursework. 3. Teaching and Assessment In general, AI tools should be gradually integrated into teaching, while fostering critical understanding and responsible use. At the same time, personal contact and progress reports should be an integral part of assessments to mitigate AI misuse. 4. Academic Integrity and Quality AI-generated content should be treated like other intellectual materials, with clear attribution to avoid plagiarism, while ensuring that students maintain their awareness and integrity. © Informatics Europe, 2025, CC BY-SA 4.0 | Page 1 Although current existing tools still have weaknesses, universities should strive to implement rigorous quality checks for AI-generated content in student submissions. Rather than solely depending on AI plagiarism detection tools, universities should incorporate oral defences/exams, or other self-auditing methods to evaluate student work. Further, effort should be made to stress the importance of quality, recognising biases and errors in AI-generated products. Students should be educated on identifying and addressing biases and errors in AI outputs, together with reinforcing their critical thinking. Additionally, it is essential to define acceptable vs. unacceptable AI use cases, establish guidelines on how students should cite AI assistance, and develop a grading approach that evaluates both AI-aided and non-AI components of the work. 5. Transparent Use Policies Universities should clearly define and communicate appropriate use guidelines for AI tools in the students' academic work, require students to indicate any use of AI tools, and include these policies in their respective Codes of Conduct. As an example, many universities have already implemented transparency guidelines in connection with student work. These guidelines require students to clearly declare how they use AI tools in preparing their work, such as home exams and project reports. 6. Personal Accountability and Societal Responsibility It is important to hold students accountable for the integrity and quality of their work, including AI-generated content. Students should always be made aware that users of AI tools are generally accountable for the quality and ethical implications of AI-generated content. Again, it is crucial to encourage critical thinking and personal responsibility in the use of AI tools. Along this line, universities should promote awareness of the ethical, legal, and societal impacts of AI. 7. Ethical Considerations Teachers should integrate discussions on the ethical use of AI into coursework and assessments. They should evaluate students by considering their understanding of the ethical, legal, and societal implications of AI. Additionally, it is important to highlight the risks associated with using these tools, such as data access and security concerns. 8. Continuous Adaptation To keep pace with the rapid advancements in AI technology, it is recommended to regularly update curricula and assessment methods. Additionally, fostering a culture of continuous learning and adaptation among students and faculty is crucial. Establishing a permanent institutional committee to review and update AI policies, along with conducting annual evaluations of AI's impact on the educational process, would ensure ongoing adaptation and maintain the long-term quality of education. © Informatics Europe, 2025, CC BY-SA 4.0 | Page 2 References: [1] Recommendations on the Use of AI in Informatics Education. Informatics Europe (2023), https://www.informatics-europe.org/component/phocadownload/category/27-recommendatio ns.html?download=198:recommendations-use-of-ai-in-informatics-education © Informatics Europe, 2025, CC BY-SA 4.0 | Page 3 For enquiries and feedback about this publication, please contact [email protected]. www.informatics-europe.org © Informatics Europe, 2025 CC BY-SA 4.0