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Authentic Assessment Pilot against Generative AI in a Faculty

Authentic assessment redesign serves as an institutional mechanism to uphold academic integrity and pedagogical validity amid generative artificial intelligence. Integrating structured tiered frameworks shifts faculty practice from discretionary experimentation into sustainable educational infrastructure. This pilot establishes scalable protocols for curriculum alignment, academic workload management, and ethical technology adoption.

Goal of work

To design and pilot an authentic assessment framework across a faculty to uphold integrity and valid evaluation in generative artificial intelligence environments.

Implementation plan

  • 1.Synthesise contemporary authentic assessment models tailored to generative AI environments.
  • 2.Develop operational governance controls and tiered task classifications for faculty modules.
  • 3.Formulate evaluation metrics and an institutional scaling roadmap.

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Research Report

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Authentic Assessment Pilot against Generative AI in a Faculty

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Executive Summary
1. Project Description and Institutional Governance Context
1.1 Drivers for Assessment Redesign in Higher Education
1.2 Faculty Strategic Alignment and Policy Framing
2. Implementation and Pedagogical Governance Controls
2.1 Tiered Integration via Assessment Scale Frameworks
3. Evaluation Metrics and Baseline Learning Indicators
3.1 Evaluating Academic Integrity and Misconduct Reduction
3.2 Authentic Performance and Critical Thinking Assurance
4. Recommendations and Faculty Rollout Priorities
4.1 Sustainable Infrastructure and Resourcing Strategy
Introduction
Conclusion
Bibliography

Introduction

The rapid expansion of generative artificial intelligence technologies poses fundamental challenges to traditional academic integrity controls and student learning evaluation across higher education faculties [1]. Prohibitionist measures and punitive institutional policies have demonstrated clear limits, driving the necessity for systemic assessment redesign grounded in real-world task authenticity [1], [2].

Transitioning from isolated academic experimentation to a coordinated faculty-wide framework requires institutional infrastructure that actively addresses staff workload, pedagogical guidance, and technological ambiguity [2]. Integrating structured assessment scales provides educators with defined pathways to incorporate synthetic tools productively while safeguarding human evaluative judgement [1].

This project establishes a pilot implementation model for authentic assessment across faculty programmes, drawing on evidence-informed pedagogical tiers to foster critical enquiry [1], [3]. The operational blueprint equips academic leadership with structured controls, capability guidelines, and scalable evaluation metrics for durable curricular reform [2].

2.1 Tiered Integration via Assessment Scale Frameworks

To transition authentic assessment redesign from discretionary academic initiative into sustainable educational infrastructure, the faculty operationalises the Artificial Intelligence Assessment Scale across all undergraduate course modules (crossref-10-4995-head26-2026-21804). The decision to implement this tiered model directly addresses persistent systemic constraints, including policy fragmentation, technological ambiguity, and academic workload pressures that emerge when assessment renewal relies solely on individual educator experimentation (crossref-10-4995-head26-2026-21804). Under this governance control, unit coordinators categorise assessment components across five defined levels spanning from 'no AI' to 'full AI' configurations (W4403409435). This graduated classification criterion requires curriculum designers to articulate explicit operational thresholds for machine assistance, ensuring that learning activities preserve essential cognitive engagement, critical thinking, and disciplined human input within task parameters (W4403409435). The practical application of the scale establishes clear boundaries for student authorship while actively supporting faculty members in redesigning course deliverables into innovative multimodal formats (W4403409435). Rather than enforcing punitive prohibitions that fail to manage digital disruption, the faculty applies these five distinct tiers to align intended disciplinary learning outcomes with transparent ethical oversight (W4403409435). Course convenors systematically employ the framework to clarify task expectations, evaluate where generative artificial intelligence tools provide constructive pedagogical enhancement, and protect evaluative tasks requiring unaided human reasoning (W4403409435; crossref-10-4995-head26-2026-21804). Consequently, embedding the tiered scale standardises pedagogical practice across diverse departments, turning authentic assessment into a scalable, accountable institutional mechanism that reinforces academic integrity throughout the broader faculty community.

References

  1. The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI-supported assessment
    Leon Furze, Mike Perkins, Jasper Roe et al.
    DOI Link
  2. Authentic Assessment in the Age of Generative Artificial Intelligence: Pedagogical Innovation to Institutional Infrastructure
    Sharon Lehane, Dr. Angela Wright, Dr. Pio Fenton
    DOI Link
  3. Integrating Online Resources and Generative Artificial Intelligence in Pharmacology Education: Implications for Curriculum Design and Assessment in Higher Education
    Miriam Moriarty
    DOI Link
  4. Embracing a new world: authentic assessment designs in an age of generative artificial intelligence (GenAI)
    Feng Su, Michael O'Dea, Margaret Wood et al.
  5. Authentic Assessment Design for Meeting the Challenges of Generative Artificial Intelligence
    Masood M Khan, Yu Dong, Nasrin Afsari Manesh

Bibliography

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Project

APA 7th Edition (Australian Implementation)