Transforming Assessments for AI in Higher Education: Empowering Faculty for Pedagogical Innovation

North-West University developed and implemented a self-paced, fully online course to build lecturer capacity for innovative, ethical, and responsible assessment reform in the age of artificial intelligence. Grounded in the University’s human-centred AI principles, the course supports lecturers in critically reflecting on module outcomes and assessment practices in light of AI.

The course comprises three structured sections and eight learning components, with lecturers earning digital badges as stackable microcredentials upon completion of each section. Guided activities, templates, and exemplars enable lecturers to redesign assessments across defined AI levels using the AI Assessment Scale (Perkins et al., 2024), while developing AI literacy, prompt engineering, critical human evaluation, reflective practice, and pedagogical innovation. The initiative positions assessment reform as a central response to AI-transformed higher education.

The best practice was implemented through a structured development model anchored in a self-paced online course, complemented by in-person institutional rollout activities.

• Institutional launch and engagement:

The initiative was introduced through ten in-person workshop days across the University’s three campuses. During these sessions, participating lecturers completed Section One of the course and earned two of the thirteen available microcredential badges, creating early engagement and momentum.

• Staff participation and incentives:

Approximately 30 percent of the University’s teaching staff completed foundational training in responsible and innovative AI use for assessment reform. Lecturers were incentivised to complete the remaining course sections through microcredential recognition and Takealot vouchers upon full completion.

• Institutional embedding:

Course completion was incorporated into lecturers’ annual performance agreements, discussed at the beginning of each academic year, embedding assessment reform into institutional quality assurance and staff development systems.

• Parallel student course:

A complementary self-paced 90-minute AI literacy course for students was introduced. Aligned with the same five AI Assessment Scale levels, the student course supports responsible AI use, critical thinking, and reflective practice, ensuring coherence between assessment design and student expectations.

Impact was measured using a comprehensive mixed-methods evaluation framework capturing engagement, learning outcomes, and assessment quality.

1. Engagement analytics and completion data:

Online platform analytics tracked lecturer participation, progress through course sections, time spent, and microcredential badge attainment, providing objective measures of uptake and sustained engagement.

2. Lecturer feedback:

Structured surveys administered during workshops and after each online course section captured lecturers’ confidence in assessment redesign, application of ethical AI principles, and shifts in pedagogical and assessment practices. Quantitative and qualitative responses were analysed to identify trends in professional growth and innovation.

3. Student feedback:

Students participating in the parallel AI literacy course reported on understanding of responsible AI use, clarity of expectations in redesigned assessments, perceived usefulness of the course, and confidence in ethical academic AI use.

4. Assessment quality review:

Redesigned assessments were reviewed using a structured rubric assessing innovation, authenticity, alignment with the AI Assessment Scale, and adherence to NWU’s AI principles. Lecturer reflections and independent reviews documented improvements in assessment design and pedagogical coherence.

Together, these measures provide evidence of strengthened educator capacity, enhanced responsible AI literacy among staff and students, and meaningful progress toward assessment reform that upholds academic integrity.

• Human-centred AI principles at institutional level
• AI Assessment Scale (Perkins et al., 2024)
• Self-paced online course with stackable microcredentials
• Digital badge and incentive structure
• Institutional leadership support and performance agreement alignment
• Parallel staff and student AI literacy development pathways