# Navigating the New Landscape: A Conceptual Model for Project-Based Assessment (PBA) in the Age of GenAI

arXiv: https://arxiv.org/abs/2508.11709 · HTML: https://mlsanity.com/paper/2508.11709/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 5 of 5)

- [Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education](https://mlsanity.com/paper/2408.01443/) (2024)
- [AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review](https://mlsanity.com/paper/2503.00079/) (2025)
- [Crafting Tomorrow's Evaluations: Assessment Design Strategies in the Era of Generative AI](https://mlsanity.com/paper/2405.01805/) (2024)
- [Experiences with Content Development and Assessment Design in the Era of GenAI](https://mlsanity.com/paper/2503.00081/) (2025)
- [Machine vs Machine: Using AI to Tackle Generative AI Threats in Assessment](https://mlsanity.com/paper/2506.02046/) (2025)

## Abstract (arXiv)

The rapid integration of Generative Artificial Intelligence (GenAI) into higher education presents both opportunities and challenges for assessment design, particularly within Project-Based Assessment (PBA) contexts. Traditional assessment methods often emphasise the final product in the PBA, which can now be significantly influenced or created by GenAI tools, raising concerns regarding product authenticity, academic integrity, and learning validation. This paper advocates for a reimagined assessment model for Project-Based Learning (PBL) or a capstone project that prioritises process-oriented evaluation, multi-modal and multifaceted assessment design, and ethical engagement with GenAI to enable higher-order thinking. The model also emphasises the use of (GenAI-assisted) personalised feedback by a supervisor as an observance of the learning process during the project lifecycle. A use case scenario is provided to illustrate the application of the model in a capstone project setting. The paper concludes with recommendations for educators and curriculum designers to ensure that assessment practices remain robust, learner-centric, and integrity-driven in the evolving landscape of GenAI.