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Authentic-Assessment Framework Implementation for an AI-Enabled Course

The integration of generative artificial intelligence into higher education necessitates a structured transition from rote testing to authentic assessment architectures. Systematic governance, combined with human-in-the-loop validation, aligns course learning outcomes with real-world evaluative tasks while safeguarding academic integrity. Operationalizing these mechanisms equips academic institutions with scalable practices for maintaining educational rigor in technology-rich environments.

Goal of work

Develop an authentic-assessment framework that operationalizes task design and governance controls in AI-enabled courses.

Implementation plan

  • 1.Review institutional governance models for generative AI adoption in academic curricula.
  • 2.Formulate authentic task design rubrics that evaluate contextual problem solving.
  • 3.Develop phased recommendations for institutional infrastructure deployment.

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Capstone Project

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Authentic-Assessment Framework Implementation for an AI-Enabled Course

Author:

Group

First M. Last

Advisor:

Dr. First Last

City, 2026

Contents

Abstract
Introduction
Project Description and Governance Context
Alignment of Learning Outcomes with Generative Tools
Institutional Policies and Academic Integrity Baselines
Implementation and Pedagogical Governance Controls
Authentic Task Design and Dynamic Rubrics
Human-in-the-Loop Formative Feedback Integration
Evaluation Metrics and Assessment Results
Diagnostic Validity and Learner Engagement Analysis
Recommendations and Curriculum Rollout Priorities
Practical Recommendations for Institutional Scaling
Conclusion
Bibliography

Introduction

The integration of generative artificial intelligence into higher education disrupts standard evaluation models and necessitates renewed assessment architectures. Contemporary coursework requires constructive alignment between automated technological capabilities and verified student competence, shifting pedagogical focus toward authentic problem solving and contextual inquiry [1].

Traditional assessment paradigms often incentivize cognitive offloading and vulnerable procedural compliance, which undermine rigorous disciplinary learning outcomes [3]. Addressing this vulnerability requires transitioning from isolated evaluative tasks toward continuous, practice-oriented frameworks that embed artificial intelligence literacy directly into learning objectives [2].

This project operationalizes an authentic-assessment implementation model across an automated course environment, synthesizing institutional governance with iterative task design. By establishing structured evaluative rubrics and human-supervised feedback mechanisms, the framework provides actionable guidelines for maintaining academic integrity while expanding real-world problem-solving competencies [3], [4].

Practical Recommendations for Institutional Scaling

Educational institutions face acute challenges when attempting to reconcile evaluative rigor with generative artificial intelligence tools. Transitioning authentic assessment from discretionary classroom experimentation into centralized institutional infrastructure provides the governance, staff capability, and pedagogical alignment necessary to mitigate generative artificial intelligence risks while sustaining rigorous evaluative integrity. Rather than relying on isolated educator initiatives that foster technological ambiguity and policy fragmentation, institutions must adopt structured criteria centered on curricular alignment, workload sustainability, and systemic ethical oversight ("Authentic Assessment," 2026). The practical decision to establish standardized authentic assessment protocols ensures that evaluation methods directly reflect authentic discipline-specific tasks, moving past superficial procedural compliance. Operationalizing this institutional transition requires embedding human-in-the-loop oversight and pedagogical governance controls directly into daily course design. As curriculum frameworks demonstrate, aligning intended learning outcomes with digital resources and generative tools necessitates continuous staff capability development and coherent pedagogical scaffolding to prevent cognitive offloading and preserve academic integrity ("Integrating Online Resources," 2026). Consequently, institutional leaders must resource dedicated instructional design support and formalized professional development pathways so that teaching faculty can systematically refine evaluative rubrics without facing excessive administrative strain. In practical application, departmental review committees evaluate course syllabi against these institutional criteria, ensuring that formative feedback mechanisms and authentic evaluative tasks actively support clinical reasoning, conceptual depth, and professional competence. By establishing shared organizational standards, academic programs protect against fragmented tool adoption, streamline evaluative workloads across instructional teams, and maintain transparent accountability standards across technology-enabled hig…

References

  1. Integrating Online Resources and Generative Artificial Intelligence in Pharmacology Education: Implications for Curriculum Design and Assessment in Higher Education
    Miriam Moriarty
    DOI Link
  2. Authentic Assessment Design for Meeting the Challenges of Generative Artificial Intelligence
    Masood M Khan, Yu Dong, Nasrin Afsari Manesh
    DOI Link
  3. Authentic Assessment in the Age of Generative Artificial Intelligence: Pedagogical Innovation to Institutional Infrastructure
    Sharon Lehane, Dr. Angela Wright, Dr. Pio Fenton
    DOI Link
  4. Empirical validation of a generative AI framework for personalized education assessment.
    Meina Qian, Hualei Ji, Lianzhi Li
  5. Generative Artificial Intelligence in Higher Education: Foundations, Curriculum, Teaching, and Assessment
    Jyotirmay Patel, Ramjeet Singh Yadav, Paramjit Singh et al.
  6. STUDENT-REFLECTED USE OF GENERATIVE ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION USING THE LEARNING EXPERIENCE DESIGN FRAMEWORK
    Robert Strohmaier

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