Wednesday, June 18, 2025

Hacking AI for Assessment or Hacking Assessment for AI?

Greetings from the Australian National University science teaching building, where Bert Verhoeven from University of Newcastle, is running us through an AI Assessment Hack-a-thon. The follows seamlessly from the EduTech Australia HE stream I chaired last week in Sydney and ACS TechUplift25 next month in Canberra. He suggests we need to learn about AI and use it in teaching. AI has limitations and Bert argues we need to teach students to use the tools critically.  

For the last few years I have been attending conferences, workshops and symposia on AI and education. Assuming I am convinced of the importance of this, where do I learn to use it for teaching? Can I sign up for a formal postgraduate university program in this, as I did to learn teaching and assessment? I did a quick search and neither of the institutions I got my most recent two qualifications in education were offering courses in AI for teaching (they did offer AI courses for computing students). There are some vocational education, but mostly for computer game development, some for business, none for education. I tried searching my friendly local TAFE, but "artificial intelligence" only comes up in the academic misconduct policy. 

There seems to be the opportunity to create courses on AI or education, perhaps also to educate academics about education. Many of the approaches to using AI in teaching and assessment are based on well known educational techniques. In effect AI is exposing poor teaching and assessment techniques. The solution is to use better techniques, which can incorporate AI.


Along the way there was discussion of program assessment structures where the student is given formative feedback in individual courses, then assessed at the end of their program. Such an approach makes sense in terms of education, but I suggest is not a viable product. As a student I am studying part time while working, so a bachelor's degree will take me six, or more, years. The risk of investing many tens of thousands of dollars for potentially no return (if I fail the final assessment) is unacceptable,. I want a return on my investment much quicker, at least within a year. As a result I will sign up where there are stacked credentials offered, even while grumbling about all the assessment. ;-)

ps: The environmental effects of AI came up. This amplifies the concern over data centers generally, in terms of energy and water use. It is not difficult to do back of the envelope calculations and I previously showed computer students how to do this. 

pps: I commented to Bert that I could not find any AI for teching courses and he suggested I ask Chat GPT to design one with the prompt "Write a course outline for a graduate course in Artificial Intelligence for Education to teach university academics in Australia.". Here is what it came up with:

Course Title:

Artificial Intelligence for Education

Course Level:

Graduate (Professional Development / Postgraduate Coursework)

Target Audience:

University academics, educational technologists, learning designers, and administrators in Australian higher education institutions.

Course Duration:

12 weeks (can be adapted to 6-week intensive format)

Delivery Mode:

Blended (Online + Optional In-Person Workshops)

Course Description:

This course equips university academics with a deep understanding of artificial intelligence (AI) and its transformative role in higher education. Participants will explore current and emerging AI technologies, pedagogical opportunities and risks, ethical considerations, policy implications, and practical strategies for integrating AI tools into learning, teaching, and assessment. Special emphasis is placed on the Australian higher education context, including alignment with TEQSA and national education policy frameworks.

Learning Outcomes:

By the end of the course, participants will be able to:

  1. Explain foundational concepts and types of AI relevant to education.

  2. Evaluate current AI applications in teaching, learning, and assessment.

  3. Design AI-enhanced learning activities and assessments.

  4. Critically assess ethical, legal, and social implications of AI use in education.

  5. Interpret Australian higher education policy in relation to AI implementation.

  6. Demonstrate leadership in responsible and innovative AI adoption at the institutional level.

Weekly Topics Overview:

Module 1: Foundations of AI in Education (Week 1–2)

  • Introduction to AI, machine learning, natural language processing, generative AI.

  • History and evolution of AI in education.

  • Overview of current tools (e.g. ChatGPT, Copilot, AI tutors, predictive analytics).

  • Case studies from Australian and global universities.

Module 2: AI in Learning and Teaching (Week 3–4)

  • AI-supported personalised learning and adaptive systems.

  • Intelligent tutoring systems and learning analytics.

  • AI in online, blended, and face-to-face modalities.

  • Impacts on curriculum design and academic roles.

Module 3: AI in Assessment and Academic Integrity (Week 5–6)

  • Automated grading, feedback generation, and formative assessment.

  • Designing AI-resilient and AI-enhanced assessments.

  • Addressing academic integrity, contract cheating, and detection tools (e.g. Turnitin AI detectors).

  • TEQSA guidance and institutional policies.

Module 4: Ethical and Societal Considerations (Week 7–8)

  • AI bias, transparency, explainability, and fairness.

  • Privacy, surveillance, and data protection (incl. Australian privacy laws).

  • Inclusion, accessibility, and equity in AI adoption.

  • Frameworks (e.g. UNESCO AI ethics, Australian AI Ethics Principles).

Module 5: Policy, Governance, and Strategic Leadership (Week 9–10)

  • Institutional AI strategies in higher education.

  • TEQSA, AQF, and other regulatory frameworks.

  • Developing policies for AI governance in teaching and learning.

  • Risk management and future-proofing universities.

Module 6: Capstone Project and Futures Thinking (Week 11–12)

  • Scenario planning: the future of AI in higher education.

  • Capstone: Propose a strategy, curriculum redesign, or policy for AI integration in your institution.

  • Peer review and reflection.

  • Pathways for ongoing development and institutional leadership.

Assessment Tasks:

  1. AI Tool Evaluation Report (20%)
    Review and critically evaluate an AI tool relevant to teaching or assessment.

  2. Discussion Journal (20%)
    Weekly reflections and engagement with peers on ethical, pedagogical, and technical issues.

  3. Case Study Analysis (20%)
    Analyse a real-world use of AI in higher education, including risks, benefits, and improvements.

  4. Capstone Project (40%)
    Develop a detailed proposal for implementing or evaluating AI in your teaching or institutional context, aligned with regulatory and ethical frameworks.

Learning Methods:

  • Weekly asynchronous modules (videos, readings, quizzes)

  • Interactive webinars with AI and education experts

  • Collaborative projects and case-based learning

  • Hands-on experimentation with AI tools (e.g. ChatGPT, Copilot, Perplexity, LMS-integrated AI)

  • Online community of practice

Recommended Readings & Resources:

  • Luckin, R. (2018). Machine Learning and Human Intelligence.

  • Selwyn, N. (2021). Should Robots Replace Teachers?

  • TEQSA & DET Reports on AI in Higher Education (Australia)

  • UNESCO and OECD Guidelines on AI and Education

  • OpenAI and Microsoft Copilot documentation

Optional Workshops (In-person or Online):

  • “Designing Assessments in the Age of AI”

  • “AI for Learning Designers and Educational Developers”

  • “Ethics and Policy Roundtables: Creating AI Governance Models”

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