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Authentic Assessment Pilot for a Faculty Facing Generative AI

The rapid proliferation of generative artificial intelligence necessitates a fundamental restructuring of higher education assessment frameworks towards authentic, competence-based evaluation. Systematic institutional implementation requires establishing robust pedagogical infrastructure to overcome faculty workload constraints and policy fragmentation. Strategic piloting of authentic assessment protocols provides academic departments with validated mechanisms to ensure rigorous learning assurance in technology-rich environments.

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Authentic Assessment Pilot for a Faculty Facing Generative AI

Author:

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First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Introduction
1. Project Description and Governance Context
1.1 Faculty Institutional Mandate and Policy Gaps
1.2 Pedagogical Drivers for Authentic Assessment Transition
2. Implementation Architecture and Governance Controls
2.1 Faculty Workload Mitigation and Redesign Strategy
2.2 Curricular Integration Protocols and AI Integrity Safeguards
3. Evaluation Metrics and Baseline Performance Indicators
3.1 Quality Assurance Criteria for Learner-Centric Evaluation
3.2 Benchmark Analysis Across Interdisciplinary Cohorts
4. Strategic Recommendations and Faculty-Wide Rollout Priorities
4.1 Institutional Infrastructure and Resource Allocation Plan
4.2 Phased Deployment Timeline and Policy Standardisation
Conclusion
Bibliography

Introduction

The rapid expansion of generative artificial intelligence across higher education institutions has created unprecedented challenges for traditional assessment modalities, exposing acute vulnerabilities in procedural compliance and academic integrity frameworks [1]. The systemic integration of automated text and code generation necessitates an institutional shift from punitive detection towards authentic assessment models that measure contextualised cognitive competencies and real-world problem-solving abilities [3].

Transitioning towards authentic assessment at faculty level requires moving beyond isolated, discretionary academic practices towards establishing robust institutional infrastructure [1]. Educational institutions face significant structural barriers, including policy fragmentation, ambiguous technological guidance, and acute academic workload constraints that impede curriculum renewal [5]. Addressing these operational challenges demands a cohesive design framework that embeds artificial intelligence literacy directly into course learning objectives.

This project establishes a pilot implementation model for a university faculty transitioning towards authentic assessment in the presence of ubiquitous artificial intelligence tools. By synthesising contemporary pedagogical frameworks and strategic policy blueprints, this project delivers actionable evaluation protocols, operational guidelines, and phased rollout mechanisms designed to safeguard academic standards while sustaining meaningful student engagement [5].

2.1 Faculty Workload Mitigation and Redesign Strategy

Executing a faculty-wide authentic assessment pilot demands a structured transition from isolated academic experimentation to robust institutional infrastructure. Rather than relying on discretionary initiatives, academic departments must adopt systematic criteria to govern assessment reform in response to generative artificial intelligence (Authentic Assessment in the Age of Generative Artificial Intelligence 2026). The primary criterion for this practical intervention is the mitigation of faculty workload through formal institutional training, directly addressing documented systemic barriers where educators report significant workload burdens alongside a lack of formal preparation (Authentic Assessment in the Age of Generative Artificial Intelligence 2026). To operationalise this baseline, the pilot integrates the Six Assessment Redesign Pivotal Strategies and the AI Assessment Integration Framework (Chan and Colloton 2024), establishing clear procedural blueprints for curriculum teams. Furthermore, this practical restructuring aligns with broader sector transitions from narrow technological validation toward sustained institutional and curricular integration (The Post-ChatGPT Research Landscape of Generative Artificial Intelligence in Higher Education 2026). By applying these redesign models across target modules, faculty leads establish transparent evaluation rubrics focused on professional competencies and contextualised problem-solving. This practical application directly counters policy fragmentation and technological ambiguity, shifting assessment governance from ad hoc adjustments toward proactive institutional infrastructure. Implementing these standardized frameworks across departmental units provides educators with sustainable pathways to embed academic integrity and artificial intelligence literacy into routine task design without expanding marking commitments.

References

  1. Authentic Assessment in the Age of Generative Artificial Intelligence: Pedagogical Innovation to Institutional Infrastructure
    Sharon Lehane, Dr. Angela Wright, Dr. Pio Fenton
    DOI Link
  2. Generative Artificial Intelligence in Higher Education: Pedagogical Potential and Risks of Using ChatGPT, Gemini, and Yandex Alice
    Anna Printsipalova
    DOI Link
  3. The Post-ChatGPT Research Landscape of Generative Artificial Intelligence in Higher Education (2023–2026): A Bibliometric Analysis
    Monica Pătruț
    DOI Link
  4. Exploring the use of generative artificial intelligence by university students: a systematic literature review
    Anna E. Korchak, Yevgeny D. Patarakin, Jamie Costley
  5. Generative AI in Higher Education
    Cecilia Ka Yuk Chan, Tom Colloton
  6. A systematic review of the impact of artificial intelligence on educational outcomes in health professions education
    Eva Feigerlová, Hind Hani, Ellie Hothersall-Davies

Bibliography

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