AI-augmented judgement in teacher performance assessment: Evidence from a human-AI moderation workflow

Zara Ersozlu; Susan Ledger; Mark Babic; Robert Parkes

doi:10.30935/cedtech/18970

Zara Ersozlu ¹ ^* , Susan Ledger ¹, Mark Babic ¹, Robert Parkes ¹

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CONT ED TECHNOLOGY, Volume 18, Issue 3, Article No: ep674

https://doi.org/10.30935/cedtech/18970

Submitted: 25 April 2026, Published Online: 15 July 2026

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Abstract

Artificial intelligence (AI) is increasingly being explored in educational assessment, but its use in high-stakes performance contexts requires careful design to support quality, consistency, and human accountability. This study designed and evaluated an AI-supported workflow to assist assessors in marking teaching performance assessment (TPA) portfolios. TPA assessors first described how they usually evaluate portfolios, and this process was used to develop a meta-level rubric logic, prompts, and workflow for the AI agent. The workflow was tested by comparing human-only marking with AI supported marking in relation to time, accuracy, cognitive load, feedback quality, usability, bias, and fairness. The findings showed that the AI-supported workflow reduced marking time and perceived workload while supporting more structured, evidence-based feedback. It also contributed to greater consistency and fairness in the moderation process. The AI agent is positioned as a “third eye” that supports human judgement. Human assessors remain responsible for making final decisions and may accept, adapt or override AI-generated suggestions.

Keywords: GenAI, human-in-the-loop, cognitive load, time efficiency, judgement support, moderation, teaching performance assessment

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Citation

Ersozlu, Z., Ledger, S., Babic, M., & Parkes, R. (2026). AI-augmented judgement in teacher performance assessment: Evidence from a human-AI moderation workflow. Contemporary Educational Technology, 18(3), Article ep674. https://doi.org/10.30935/cedtech/18970

Ersozlu, Z., Ledger, S., Babic, M., and Parkes, R. (2026). AI-augmented judgement in teacher performance assessment: Evidence from a human-AI moderation workflow. Contemporary Educational Technology, 18(3), ep674. https://doi.org/10.30935/cedtech/18970

Ersozlu Z, Ledger S, Babic M, Parkes R. AI-augmented judgement in teacher performance assessment: Evidence from a human-AI moderation workflow. CONT ED TECHNOLOGY. 2026;18(3), ep674. https://doi.org/10.30935/cedtech/18970

Ersozlu, Zara, Susan Ledger, Mark Babic, and Robert Parkes. "AI-augmented judgement in teacher performance assessment: Evidence from a human-AI moderation workflow". Contemporary Educational Technology 2026 18 no. 3 (2026): ep674. https://doi.org/10.30935/cedtech/18970

Ersozlu, Zara et al. "AI-augmented judgement in teacher performance assessment: Evidence from a human-AI moderation workflow". Contemporary Educational Technology, vol. 18, no. 3, 2026, ep674. https://doi.org/10.30935/cedtech/18970

Ersozlu Z, Ledger S, Babic M, Parkes R. AI-augmented judgement in teacher performance assessment: Evidence from a human-AI moderation workflow. CONT ED TECHNOLOGY. 2026;18(3):ep674. https://doi.org/10.30935/cedtech/18970