AI-Enhanced Teaching, Learning Design and Assessment
Designing AI-enhanced learning experiences
Description
| Module No. | Module 1 · Module 2 · Module 3 · Module 4 · Module 5 |
| Subtopic 5.1 | Designing AI-Enhanced Learning Experiences |
| Artificial Intelligence Literacy Component | AI integration in education; human–AI collaboration in learning design; instructional alignment. |
| Decision-Making Skills | Selecting AI-supported learning strategies; evaluating usefulness and feasibility of AI. |
| Teacher Learning Outcome | Designs AI-enhanced learning experiences for STEM higher education aligned with module outcomes. |
| Student Learning Outcome | Makes justified decisions about how AI can support their own learning. |
| Possible Tools | ChatGPT; Gemini; Claude; LMS tools. |
| AI Literacy Level | Basic |
| Estimated Time | 90 minutes in total: a 30-minute faculty development session (Section B) and a 60-minute student activity (Section D), plus optional independent preparation |
Introduction
Many teaching decisions in STEM higher education concern how learning is supported: which explanations, practice, feedback and pacing a diverse group of students receives. Designing an AI-enhanced learning experience is the deliberate use of artificial intelligence to provide that support where it is otherwise scarce, in a way that serves a defined module outcome. This section gives the teacher the primary knowledge needed to make and justify such designs before running the session.
The starting point is the recognition of individual differences: because the learning process is different for each and every learner, adaptive educational systems combine two capabilities – comprehending and detecting a person's specific learning needs, and applying appropriate pedagogy to enhance the learning process (Colchester et al., 2017). The teacher therefore does not "add a tool"; they decide which adaptive function to attach to a concrete outcome.
Using generative AI for teaching and learning
Description
| Module No. | Module 1 · Module 2 · Module 3 · Module 4 · Module 5 |
| Subtopic 5.2 | Using Generative AI for Teaching and Learning |
| Artificial Intelligence Literacy Component | Generative AI functions; text/image/code generation; limitations of generative AI; content adaptation. |
| Decision-Making Skills | Evaluating appropriateness of AI use; comparing AI vs non-AI strategies. |
| Teacher Learning Outcome | Uses generative AI to develop learning materials, examples, activities and support resources. |
| Student Learning Outcome | Evaluates when and how generative AI can appropriately support learning. |
| Possible Tools | ChatGPT; Gemini; Claude; Canva AI; GitHub Copilot. |
| AI Literacy Level | Basic |
| Estimated Time | 90 minutes in total: a 30-minute faculty development session (Section B) and a 60-minute student activity (Section D), plus optional independent preparation |
Introduction
This subtopic equips teachers to develop learning materials, examples, activities and support resources with generative AI. Functionally, generative tools explain and re-explain concepts at different levels, generate worked examples, practice tasks and scenarios, summarise and repeat topics on demand, produce visual materials and generate or explain code – functions that map directly onto the adaptation mechanisms of adaptive-systems theory: feedback, sequencing and varied presentation (Colchester et al., 2017). Students themselves describe chatbots as helping to summarise, repeat and "lecture" a topic (Cheung, 2026).
The decisive professional skill is not producing material but judging and curating it: knowing when generative AI adds value and when it must be verified or avoided. This section gives the teacher that judgement, grounded in the empirical record of benefits and limitations.
AI-supported assessment and learning analytics
Description
| Module No. | Module 5 – AI-Supported Green Decision-Making (AIGUIDE) |
| Subtopic 5.3 | AI-Supported Assessment and Learning Analytics |
| Artificial Intelligence Literacy Component | Learning analytics; AI-based feedback systems; data-driven assessment basics; formative feedback. |
| Decision-Making Skills | Interpreting feedback; making improvement decisions based on learner evidence. |
| Teacher Learning Outcome | Uses AI-supported assessment and learning analytics to monitor and support learning. |
| Student Learning Outcome | Interprets AI-generated feedback or assessment outputs for improvement. |
| Possible Tools | LMS analytics; rubric tools; AI feedback tools. |
| AI Literacy Level | Medium |
| Estimated Time | 90 minutes in total: a 30-minute faculty development session (Section B) and a 60-minute student activity (Section D), plus optional independent preparation |
Introduction
This subtopic gives teachers the primary knowledge for monitoring and supporting learning with AI-supported assessment and analytics. Learning analytics and educational data mining convert the data continuously produced by learning environments (Moodle-type LMSs, portals, MOOCs) into insight: continuous monitoring detects a struggling student at the earliest moment and pinpoints the parts of a course causing the greatest difficulty, while students can visualise progress and reach appropriate resources (Colchester et al., 2017).
The decisive competence is analytical literacy – knowing what a metric measures, how certain it is, what alternative explanations exist, and what decision it warrants. This section builds that literacy, because the field's own diagnosis is that limited educator knowledge, not missing technology, is the main barrier (Colchester et al., 2017).
Critical, ethical and sustainable use of AI in education
Description
| Module No. | Module 5 – AI-Supported Green Decision-Making (AIGUIDE) |
| Subtopic 5.4 | Critical, Ethical and Sustainable Use of AI in Education |
| Artificial Intelligence Literacy Component | AI ethics; data privacy; transparency; fairness; sustainability and environmental implications of AI use. |
| Decision-Making Skills | Ethical reasoning; evaluating consequences; responsible educational decision-making. |
| Teacher Learning Outcome | Identifies critical, ethical and sustainability-related issues in AI-enhanced teaching and classroom implementation. |
| Student Learning Outcome | Makes ethically justified decisions about the use of AI in learning. |
| Possible Tools | Chatbots; image AI tools; AI policy templates. |
| AI Literacy Level | Medium |
| Estimated Time | 90 minutes in total: a 30-minute faculty development session (Section B) and a 60-minute student activity (Section D), plus optional independent preparation |
Introduction
This subtopic gives teachers the map needed to identify critical, ethical and sustainability-related issues in AI-enhanced teaching. The ethical framework (IEEE Ethically Aligned Design; EU ethics guidelines for trustworthy AI) demands transparency, accountability, fairness and privacy (Vinuesa et al., 2020). Because a single ethical rule-set is not advisable in complex contexts (Vinuesa et al., 2020), the aim is reasoned deliberation on cases, not a list of prohibitions.
Each classroom AI use is audited through five lenses – ethics, privacy, transparency, fairness, sustainability – and the reasoning is recorded in a working AI policy. This section equips the teacher to run that audit and to teach it.
Assessment
Quiz
Quiz coming soon.