ACCOUNTABILITY & LEADERSHIP IN AI

Shared Language

A scenario to explore the need for shared language when Responding to Generative AI through Assessment Reform

How to cite this learning scenario

Arantes, J. (2025). Shared Language. www.AI4education.org. Licensed under a Creative Commons Attribution 4.0 International License.
abstract
This case study explores how the rise of generative AI has catalyzed calls for a unified and coherent approach to curriculum and assessment reform in Australian higher education. Drawing from sector-wide responses to the disruptive potential of AI, it highlights emerging frameworks—such as programmatic assessment and two-lane models—as vehicles for systemic change. Through comparative analysis and structured learning tasks, the case invites critical engagement with policy, practice, and pedagogy, while advocating for a shared language and national framework to guide future-ready, AI-responsive assessment.

In the absence of a shared language, even the best intentions in assessment reform can fracture—what AI disrupts most is not just what we do, but how well we understand each other when we do it.

Programmatic Possibilities

At South Central University (SCU), the Faculty of Education launches a curriculum renewal initiative in response to rising concerns about AI-generated assignments. A cross-functional team is formed to embed "programmatic assessment" across teacher education degrees. At the same time, the Faculty of Health Sciences—operating independently—announces its own shift to "program-level assessment" in its allied health programs. Six months in, tensions surface. The Education team assumes a developmental model with low-stakes, cumulative assessments and narrative judgment. Meanwhile, Health Sciences views their work as aligning isolated assessments with graduate attributes—without altering task types or feedback strategies. Confusion arises during cross-faculty academic board presentations, as shared terms reveal divergent practices. Student support teams raise concerns about inconsistent feedback and unclear progression paths. The Deputy Vice Chancellor (Academic) requests a review. As AI use escalates among students, the university realises its disjointed approach undermines both policy clarity and the integrity of graduate learning outcomes.

Potential Research Topics

Potential Research Questions

This case prompts educational leaders to reflect on what structures, roles, and responsibilities are needed to safely and successfully manage AI use across their institutions.
Differentiate between programmatic approach, program-level assessment, programmatic assessment, and programmatic assessment for learning using real-world educational examples. Identify the risks of misaligned assessment strategies in higher education settings, especially in the context of AI integration. Analyse the role of institutional leadership and policy in driving unified curriculum transformation and AI governance. Design an action plan to support shared terminology and cross-faculty collaboration on assessment transformation initiatives. Apply systems thinking to propose AI-responsive assessment models that promote ethical, fair, and future-facing educational practices.
What are the implications of inconsistent definitions across faculties when responding to AI challenges in assessment? How does a shared language support or hinder the implementation of future-ready assessment? What role does leadership play in aligning curriculum reform across an institution? In what ways could generative AI exacerbate existing inconsistencies or risk fragmentation of assessment systems?

Data collection Prompts

Activity 1: Compare and Contrast Frameworks (5.1, 5.2) Task: Using the definitions provided, identify how your institution currently approaches assessment reform. Which model(s) are implicitly or explicitly being used? Map practices to each term. Identify gaps, overlaps, or misalignments. Activity 2: Policy Lab – Build a Shared Framework (5.5, 7.4) Task: In teams, create a draft version of a shared national framework for programmatic assessment. Include: Terminology, guiding principles, and sample policy language. Consider discipline differences and institutional flexibility. Activity 3: ‘Declutter the Terms’ Debate (6.3) Task: Host a structured debate on the proposition: "Without sector-wide definitions, curriculum transformation will always fall short." Reflect on whether standardisation might support or stifle innovation. 

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Acknowledgement of CountryWe acknowledge the Ancestors, Elders, and families of the Kulin Nation, who are the Traditional Owners of the land where this work has been predominantly completed. As we share our own knowledge practices, we pay respect to the deep knowledge embedded within the Aboriginal community and recognise their custodianship 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 continue to have a unique role in the life of this region.
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