What are some positive ways to incorporate AI into your course?
Responsible Integration of AI in University Education
Opportunities and Challenges
Artificial intelligence offers new opportunities to enhance individual and groupwise learning. However, integrating AI into education is complex and requires careful pedagogical and organisational planning.
Oversight and Evaluation
To ensure responsible use, AI systems must undergo continuous oversight and evaluation. For example, regular reviews of AI-supported assessment tools can help identify bias or inaccuracies. At the same time, pilot implementations allow institutions to assess both the benefits and limitations of AI before large-scale adoption.
User-Centered Technology
A user-centred approach requires that technology responds to the needs of students and teachers. IT departments should provide technical support and ensure data protection, for instance, by safeguarding student records. At the same time, teachers should collaborate by sharing experiences and best practices when integrating AI tools into their courses.
- Use AI wisely.
- Think before you follow a new AI option.
- Tech should fit your needs.
- AI helps, but it is not magic
Current AI-use by many students (early adopters)
1.Information search: Students use AI to quickly and efficiently find relevant information for their courses, research projects and knowledge questions.
2.Grammar checking and enhancing writing skills: AI-powered writing assistants, such as Grammarly, help students enhance their writing.
3.Summarising and paraphrasing documents: AI tools are used to shorten long articles and research reports into concise summaries and to rephrase text while retaining the original meaning.
4.Creating first drafts: Students use AI to generate drafts of essays, reports and other written assignments, providing a starting point for their own writing and editing.
5.Explaining complex concepts: AI tutors and chatbots help students understand difficult concepts by providing clear explanations, examples, and step-by-step guidance.
6.Suggesting research ideas: AI tools can generate research topics, identify relevant sources, and explore different perspectives on a subject.
Faculty are exploring various ways to use AI in their work
- Supporting curriculum design: AI can assist faculty in designing more effective and engaging curricula by analysing student data, identifying learning gaps, and suggesting relevant resources.
- Automating administrative workflows: AI can automate repetitive administrative tasks, such as grading, scheduling, and communication, freeing up faculty time for more meaningful activities like teaching and research.
- Enhancing teaching practices: AI can be used to create personalised learning experiences, provide targeted feedback, and adapt instruction to individual student needs.
- Personalising learning: AI-powered learning platforms can tailor content, pacing, and activities to each student’s learning style and preferences, creating a more individualised and effective learning experience.
What is Generic Artificial Intelligence?
Key idea: Generative AI is a specific type of AI that can automatically produce text, images, videos, audio, and other content.
For example, Copilot, ChatGPT, Google Gemini, Anthropic Claude and Meta Llama.
In this website the focus on generic AI
What is Non Generic Artificial Intelligence?
Key idea: NON-generative AI does not
create new content. It recognises, predicts, sorts or classifies what already
exists.
For example:
Classification: is this email spam or not (AI chooses a category)(no new content).
Recognition: What objects are in this picture? AI identifies what already exists.
Summarisation: (condensing only) Summarise this text in three bullet points.AI reduce information, no new ideas.
Active learning help to apply AI as achieve the requested academic level
- Students actively study in class or on their own to achieve their learning goals, not just listen. Active learning supports deeper understanding (AI).
- Students apply their knowledge in the classroom or self-study assignments and receive feedback afterwards. (AI)
- The teacher’s expectations for student learning are clearly explained in the coursebook ( Focus on Theory and Practice).
- ´Teachers and AI support self-study, but students become more independent over time (scaffolding).
A practical principle is Time-on-task: more meaningful study leads to more learning. Teachers should find meaningful activities.
Examples of applying Generic AI in the learning activities of your course
AI offers a range of learning activities for academic use. The AI options are consistently positioned within the learning process.
The list is prepared together with CoPilot. A cross-national synthesis (Europe – China – Indonesia – Malaysia).
The AI Assessment Scale provides guidance for assessing students who have applied AI tools. See below.
- Text Analysis and Critical Evaluation
Where the AI idea is applied: AI generates comparison texts, summaries, or alternative formulations that students analyse for accuracy, bias, and coherence. The AI output functions as a contrasting reference point that students must verify, critique, and contextualise using academic sources.
AI Scavenger Hunt (VU Amsterdam)
Students compare multiple texts on the same topic, including an AI-generated text.
Focus: recognising AI-generated text, evaluating sources, and identifying bias and inaccuracies.
Reverse Engineering Prompts (VU Amsterdam)
Students receive an AI-generated text and reconstruct the prompt that could have produced it.
Focus: prompt engineering, understanding AI generation processes, and reproducibility.
AI-Supported Literature Exploration (NL & Malaysia)
Students use AI to create an initial topic overview, then verify claims with academic sources.
Focus: research skills, understanding limitations of AI summaries and academic transparency.
- Argumentation, Debate, and Ethical Reflection
Where the AI idea is applied: AI produces arguments, counter‑arguments, or simulated stakeholder perspectives that students evaluate for quality, bias, and ethical implications. The AI output serves as a stimulus for deeper reasoning, structured debate, and ethical reflection.
AI-Supported Debate with Statements (VU Amsterdam)
Students generate arguments with AI and critically assess their quality and bias.
Focus: critical thinking, argumentation, ethical reasoning.
AI-Based Simulations (NL, China)
Students interact with AI-generated stakeholders (e.g., patients, policymakers).
Focus: applying theory to practice, decision-making and ethical reflection.
- Feedback, Writing Support, and Formative Assessment
Where the AI idea is applied: AI provides draft feedback, structural suggestions, or language improvements that students assess, refine, and integrate into their revisions. The AI output supports iterative writing and strengthens students’ ability to judge the quality of feedback.
AI-Supported Peer Feedback (NL, Malaysia)
AI provides feedback on drafts; students evaluate and revise based on its suggestions.
Focus: feedback literacy, academic writing and critical evaluation of AI.
AI-Supported Academic Writing Labs (China)
AI gives structured feedback on argumentation and language; students compare it with peer feedback.
Focus: writing development, metacognition, quality control.
AI-Based Formative Assessment (Indonesia)
AI provides instant feedback on short essays; students validate it with real sources.
Focus: assessment literacy, verification skills and academic integrity.
- Classroom Analytics and Interactive Teaching
Where the AI idea is applied: AI aggregates, clusters, or summarises student responses in real time, enabling rapid insight into collective understanding. The AI output serves as a diagnostic layer that informs both teachers’ decisions and students’ reflections during class.
The Group Analyser (Erasmus University Rotterdam)
AI summarises live student responses during class.
Focus: metacognition, interactive teaching, rapid feedback loops.
AI-Enhanced Classroom Analytics (China)
AI aggregates and visualises student responses at scale.
Focus: adaptive teaching, data-informed instruction.
- Creative and Design-Oriented Learning
Where the AI idea is applied: AI generates ideas, scenarios, constraints, or prototypes that students critique, adapt, and extend. The AI output functions as a creative catalyst, supporting divergent thinking, design iteration, and the evaluation of alternative solutions.
AI as a Brainstorming Partner (NL)
Students generate, refine, and evaluate ideas with AI.
Focus: divergent thinking, creativity, and idea evaluation.
AI-Generated PBL Scenarios (Malaysia)
AI creates realistic constraints and cases for problem-based learning.
Focus: scenario analysis, realism checks, problem-solving.
AI-Supported Entrepreneurship Labs (Indonesia)
AI helps generate business ideas; students assess their feasibility and ethical implications.
Focus: innovation, entrepreneurial reasoning, ethical reflection.
- Learning Reflection and Metacognition
Where the AI idea is applied: AI is used to externalise and structure reflection, while students evaluate accuracy and completeness.
Guided Learning Reflection with AI (Europe, Netherlands)
Students use AI to summarise their own learning process based on drafts, feedback, or discussion notes, and then critically revise the summary.
Focus: metacognition, self-regulated learning, reflective writing.
- Multilingual and Inclusive Academic Learning
Where the AI idea is applied: AI supports access to academic content, not interpretation or judgment.
Language Mediation and Concept Clarification (Europe, Malaysia, Indonesia)
Students use AI to translate, simplify, or rephrase academic texts and compare versions to identify shifts in meaning or emphasis.
Focus: inclusivity, academic language development, epistemic awareness.
- Data Interpretation and Verification
Where the AI idea is applied: AI simulates analytical outputs that students must validate.
AI‑Generated Data Interpretation Tasks (Europe, China)
AI produces summaries or interpretations of datasets; students verify claims and reconstruct the underlying data logic.
Focus: data literacy, methodological reasoning, verification skills.
- Collaborative Knowledge Synthesis
Where the AI idea is applied: AI supports collective sense-making, not decision-making
Group Synthesis with AI Mediation (Netherlands, China)
AI aggregates group inputs (ideas, arguments, questions) into a draft synthesis that students critique and refine collaboratively.
Focus: collaboration, synthesis skills, epistemic negotiation.
- Assessment Literacy and Transparency
Where the AI idea is applied: AI is used to surface misunderstandings about assessment expectations.
Students ask the AI to explain assessment rubrics and to compare its interpretations with the teacher’s intent.
Interpreting Assessment Criteria with AI (Europe, Malaysia)
Focus: assessment literacy, transparency, student agency.
11. Ethical Boundary Testing
Where the AI idea is applied: AI becomes an object of critique rather than a helper.
Exploring Bias and Failure Modes (Netherlands, China)
Students deliberately prompt AI to generate biased, incomplete, or ethically problematic outputs and analyse the reasons for these failures.
Focus: ethics, bias awareness, responsible AI use.
- Curriculum and Lesson Design (Teacher Education)
Where the AI idea is applied: AI supports design ideation, while humans judge educational validity.
Course and Lesson Design Studios (Europe, Indonesia)
Students or trainee teachers use AI to propose alternative course structures, learning outcomes, or lesson plans and evaluate pedagogical quality.
Focus: curriculum literacy, pedagogical reasoning, design thinking.
The AI Assessment Scale provides guidance how to assess students who have applied AI tools
References
1. Mulford, March 6 2025, AI In Higher Education: A Meta-Summary of Recent Surveys Of Students And Faculty. Campbell Academic Technology Services, USA
2.AI texts for teachers from the University of Leiden, the Netherlands and the University of Amsterdam.
3. Roe, Perkins, Tregubova & Cook, (August 2024), A preprint. The eap-aias: Adapting the AI Assessment Scale for English for Academic Purposes. University of Singapore, British University Vietnam. jasper.Roe@jcu.Edu.Au .
4 . Mike Perkins, Leon Furze, Jasper Roe, and Jason MacVaugh. (2024)The Artificial Intelligence Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment. Journal of University Teaching & Learning Practice 1 (6). British University Vietnam, Vietnam; Deakin University, Australia; James Cook University, Australia
5.Furze, L., Perkins, M., Roe, J., & MacVaugh, J. ( 024). The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI-supported assessment. Australasian Journal of Educational Technology. https://doi.org/10.14742/ajet.9434
