About this course
A professional micro-credential for university educators who want to use generative AI with purpose and protect the validity of their assessment. You will build a working understanding of how large language models behave, apply international guidance on responsible AI, design human-AI collaborative learning activities, and redesign assessment so that it is authentic, process-visible and resilient to AI misuse.
Who it is for
Faculty, programme leaders, learning designers and anyone who sets or marks assessment in higher education. No technical background is needed.
What you will be able to do
- Explain how generative AI produces outputs and analyse its characteristic failure modes
- Apply international principles of responsible, ethical and equitable AI use in teaching
- Construct and refine prompts that produce accurate, discipline-appropriate teaching materials
- Design learning activities in which AI deepens, rather than replaces, student thinking
- Evaluate the validity of existing assessments in an AI-rich environment
- Create authentic, process-visible assessment and a clear AI use statement for a course
Course outline
Module 1: Generative AI Foundations: From AI Literacy to AI Fluency
How large language models work, where they fail, and the principles that govern their responsible use in higher education.
Topic 1.1: Inside the Machine: How Large Language Models Work
- Explain how large language models generate text through pre-training, fine-tuning and next-token prediction
- Analyse the characteristic failure modes of generative AI, including hallucination, bias and sycophancy
- Evaluate generative AI tools for their fitness for a specific teaching purpose
Topic 1.2: Responsible AI: Ethics, Equity and Governance
- Describe the principal international frameworks that guide responsible AI use in education
- Apply data protection and intellectual property principles when using AI with student work
- Analyse equity risks in AI-enabled teaching and assessment, including access gaps and detector bias
Module 2: AI-Augmented Pedagogy: Designing for Human-AI Collaboration
Prompting for teaching, and learning activities in which AI deepens rather than replaces student thinking.
Topic 2.1: Prompt Engineering for Pedagogy
- Construct structured prompts that specify role, context, task, format and constraints
- Apply iterative prompting techniques, including examples and step-by-step reasoning, to refine teaching materials
- Evaluate AI-generated teaching content for accuracy, alignment and inclusiveness before use
Topic 2.2: AI-Enhanced Learning Design
- Explain the difference between cognitive offloading and productive struggle in AI-supported learning
- Design learning activities that use AI as tutor, coach, simulator or critic to deepen student thinking
- Apply scaffolding and Universal Design for Learning to support diverse learners in using AI effectively
Module 3: Assessment Reimagined: Authentic, AI-Resilient and Evidence-Based
Validity, AI use scales and the redesign of assessment so it still shows real learning.
Topic 3.1: Assessment Validity in the Age of Generative AI
- Explain why assessment validity, rather than detection, is the foundation of academic integrity with AI
- Apply the AI Assessment Scale to specify and communicate permitted AI use in each assessment
- Analyse an existing assessment for vulnerability to AI substitution and threats to validity
Topic 3.2: Designing Authentic and Process-Visible Assessment
- Create an authentic, process-visible assessment that evidences individual learning
- Apply AI responsibly to feedback and rubric-based marking while maintaining human judgement
- Create a course AI use statement and a plan for communicating it to students
How you earn the credential
The course is organised module by module, topic by topic and outcome by outcome. Each of the 18 learning outcomes ends with a Knowledge Check, each of the 6 topics closes with a Skill Checkpoint, and each of the 3 modules closes with a Mastery Milestone. The course ends with the 50-question Capstone Credential Assessment: score 70% or more and your certificate is issued straight away, with a unique ID and a QR code that opens a public verification page. In total the course contains 260 assessment questions.
Credential details
- Level: Postgraduate continuing professional development (learning outcomes comparable to EQF level 7)
- Field: Education: higher education teaching, learning and assessment
- Prerequisites: None. Designed for teaching staff in any discipline.
- Cost: Free, self-paced and online
Frequently asked questions
Is AI in Teaching and Assessment free?
Yes. AI in Teaching and Assessment is a free, self-paced online course from FlipPraxis Educator Edge, including the final assessment and the verifiable certificate.
How long does the course take?
About 10 learning hours, organised into 3 modules, 6 topics and 18 learning outcomes. Your progress is saved, so you can study at your own pace.
Who is this course for?
Faculty, programme leaders, learning designers and anyone who sets or marks assessment in higher education. No technical background is needed.
How do I earn the certificate?
Complete each learning outcome and its Knowledge Check, then pass the 50-question Capstone Credential Assessment with a score of 70% or more. The certificate is issued straight away with a unique ID and a QR code for public verification.
Do I need any prior experience?
None. Designed for teaching staff in any discipline.