Governing GenAI Standards: Engaging Accreditation Bodies and External Stakeholders Aligning Future-Ready Assessment Reforms with Professional Accountability

ENGAGEMENT & DEMOCRATIC GOVERNANCE IN AI

How to cite this learning scenario

Arantes, J. (2025). Governing GenAI Standards: Engaging Accreditation Bodies and External Stakeholders. www.AI4education.org. Licensed under a Creative Commons Attribution 4.0 International License.
abstract
This scenario explores how institutions can responsibly integrate GenAI into assessment reform while maintaining strong alignment with existing accreditation and regulatory frameworks. It highlights the role of good governance — based on transparency, consultation, and risk management — in ensuring that technological innovation supports, rather than undermines, established professional standards. The scenario invites critical reflection on how governance structures can proactively maintain trust and compliance during rapid technological change.

"Innovation succeeds not when it breaks standards, but when it strengthens them through deliberate alignment."

Innovation Within Boundaries

Your institution has embarked on a major assessment reform initiative that integrates Generative AI (GenAI) technologies to support rubric generation, academic integrity checking, and personalised feedback processes. While internally the reform is seen as a major innovation, leadership is acutely aware that all changes must remain fully compliant with accreditation and professional standards already governing your courses. Rather than pushing for new standards, leadership adopts a governance model that treats existing accreditation frameworks as guiding pillars. You are invited to join a cross-functional taskforce, which includes teaching academics, accreditation officers, library staff, students, and external compliance consultants. The taskforce’s role is to ensure that all uses of GenAI — whether in assessment design, delivery, or moderation — can be mapped directly against accreditation criteria such as transparency, validity, reliability, academic integrity, and professional ethics. Each stage of the GenAI reform is accompanied by a documented mapping exercise, explicitly demonstrating how the new practices uphold accreditation requirements. Consultation sessions with accrediting bodies are built into the governance timeline, allowing early dialogue, feedback loops, and written endorsements of reform elements before full implementation. Where GenAI presents novel risks — such as potential undermining of human judgement — the governance framework mandates human validation checkpoints and student education on ethical AI use as a non-negotiable layer of assessment practice. You must now consider: how do you ensure that innovation stays agile without risking compliance? How can governance structures maintain clear audit trails showing alignment with standards? How do you keep staff, students, and external bodies equally confident that GenAI strengthens — rather than weakens — educational integrity?

ResearchTopics

Research Questions

Governance strategies for ensuring accreditation compliance in GenAI assessment reforms Mapping GenAI-enabled assessment to existing professional and ethical standards Risk management frameworks for GenAI in accredited education programs Building accreditation-ready audit trails during educational innovation Maintaining trust with regulators and stakeholders during AI-driven reform
How can institutions govern GenAI reforms to ensure full compliance with existing accreditation standards? What mapping strategies best demonstrate alignment between GenAI assessment tools and accreditation criteria? How can governance structures manage the tension between innovation and compliance? In what ways does transparent consultation with accrediting bodies impact reform legitimacy? What governance practices best safeguard human oversight in AI-assisted assessment systems?

Data collection

Practicing teachers could collect data by maintaining compliance checklists linking each GenAI-assisted assessment back to accreditation criteria. TAFE teachers could collect data by participating in peer audits comparing GenAI-integrated assessment designs to external standards. Higher education academics could collect data by conducting reflective analyses of how GenAI moderation processes align with professional expectations. Researchers could collect data through interviews with accreditation officers on perceptions of AI use in validated programs. Leaders could collect data by auditing reform outcomes against accreditation performance indicators over multiple review cycles.
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Acknowledgement of Country We acknowledge the Ancestors, Elders and families of the Kulin Nation (who are the traditional owners of the land. where this work has bene predominantly completed. As we share our own knowledge practices, we pay respect to the deep knowledge embedded within the Aboriginal community and recognise their ownership of Country. We acknowledge that the land on which we meet, learn, and share knowledge is a place of age-old ceremonies of celebration, initiation and renewal, and that the Traditional Owners' living culture and practices have a unique role in the life of this region.