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#human-computer interaction Preprint Open access

Scaling Peer Assessments: An Integrity Report from a Large Engineering Internship

Jinal Gupta Pavani Ayinampudi Aditya B. M. V. Prakash Hegade Rohit Sharma Sakshi Sharma Meenakshi V S. R. S. Iyengar
Oct 2026
Human-computer Interaction

Abstract

Assessing learning in large classrooms presents a significant challenge for individual instructors, who may have limited capacity to evaluate the understanding, participation, and assessment behaviour of every student. Peer assessments have been a way of distributing this responsibility among learners, allowing them to evaluate and provide feedback to one another while reducing dependence on instructor-led assessments. Building on this approach, we implemented a peer validation model within a large, multi-institutional internship programme in which students who demonstrated sufficient understanding were authorised to assess and validate their peers through short oral discussions. The assessment process began with the instructor validating a small group of students, who were then authorised to validate their peers, allowing the process to gradually expand across the cohort and operate at scale. This study examines how participants experienced the model and the extent to which assessment integrity was maintained, using an end-of-programme survey of 238 consenting respondents. Most participants regarded the activity as worthwhile, with 79.8% reporting that they solved problems they could not previously solve. However, 29.0% acknowledged at least one instance of reduced effort, a lowered validation standard, or reciprocal validation, while 88.7% believed that at least a little validation had occurred without proper examination. When asked how the process could be strengthened, participants selected post-validation discussion of solutions approximately twice as often as closer auditing or mentor-led validation. These findings provide descriptive evidence of both the potential and the integrity challenges of using peer validation as a scalable assessment approach in large learning environments.

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