Research Article
Zara Ersozlu, Susan Ledger, Mark Babic, Robert Parkes
CONT ED TECHNOLOGY, Volume 18, Issue 3, Article No: ep674
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
Research Article
Gulnur Ussenova, Gulnara Issayeva, Ryskul Urazaliyeva, Galina Karimova, Marina Mukhanova
CONT ED TECHNOLOGY, Volume 17, Issue 4, Article No: ep618
ABSTRACT
This study investigates the extent to which the use of digital educational resources (DER) predicts students’ cognitive independence (CI) in higher education and explores the moderating roles of psychological variables; motivation for digital learning, self-regulation skills (SRS), and cognitive engagement (CE). A total of 276 undergraduate students from Korkyt Ata Kyzylorda University in Kazakhstan participated in the study. Data were collected using a validated survey instrument covering five constructs: DER usage, motivation, self-regulation, CE, and CI. Moderation analyses were conducted using general linear models. The results revealed that DER usage is a significant positive predictor of CI. While motivation, self-regulation, and engagement were each strong direct predictors, only SRS and CE moderate the relationship between DER usage and CI. Specifically, students with lower levels of self-regulation or engagement benefited more from DER use. These findings show the compensatory role of digital tools in improving autonomy among less-prepared learners. The study contributes to the literature by identifying for whom DER is most effective and indicates the need for differentiated digital pedagogical strategies that promote independent learning.
Keywords: cognitive engagement, cognitive independence, digital educational resources, higher education, moderation analysis, motivation, self-regulation