Assessment Authenticity in Generative-AI-Rich Learning Environments

Riya Sen

Assessment Authenticity in Generative-AI-Rich Learning Environments

Author(s)	Dr. Riya Sen
Country	United States
Abstract	Higher education is increasingly expected to prepare students not only for employment but also for informed participation in the social, civic, environmental, and institutional challenges affecting their communities. Conventional classroom instruction can develop disciplinary knowledge, yet students may have limited opportunities to apply that knowledge to complex problems involving multiple stakeholders, incomplete evidence, competing interests, contextual constraints, and consequences for real communities. Community-embedded curriculum responds to this limitation by integrating community-defined problems, reciprocal partnerships, interdisciplinary inquiry, collaborative problem solving, structured reflection, and public-facing outcomes within credit-bearing academic learning. A 2020 meta-analysis of community-engaged learning in higher education found statistically significant positive effects on academic, social, and citizenship outcomes, providing contemporary evidence that community engagement can contribute to multiple dimensions of student development. This study develops a Community-Embedded Civic Problem-Solving framework that distinguishes genuine co-design with community partners from projects in which communities are used primarily as sites for student learning. Because no authentic university-community dataset was provided, the quantitative component is explicitly simulation-based. A synthetic dataset of 300 hypothetical university students was constructed across limited, collaborative, and co-designed curriculum-embeddedness levels. The simulated relationship between curriculum embeddedness and civic problem-solving competence was strongly positive (r = 0.760). Students represented in the co-designed category demonstrated higher simulated problem-solving, reflective-learning, and community-responsiveness scores than students in the limited-engagement category. These numerical outcomes are illustrative rather than empirical population estimates. The paper concludes that community-embedded curricula are most educationally and ethically defensible when community priorities influence problem definition, students integrate disciplinary evidence with local knowledge, reciprocal partnerships replace one-directional service, reflection links experience with theory, and community partners participate meaningfully in evaluating the usefulness of student work.
Keywords	community-embedded curriculum; civic problem solving; community-engaged learning; service-learning; experiential education; higher education; civic engagement; community partnerships; problem-based learning
Field	Engineering
Published In	Volume 3, Issue 6, November-December 2022
Published On	2022-11-15

View / Download PDF File

About AJAMIR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@ajamir.com

American Journal of Advanced Multidisciplinary Innovation and Research

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Assessment Authenticity in Generative-AI-Rich Learning Environments

Share this