Artificial intelligence on the advance to enhance educational assessment: Scientific clickbait or genuine gamechanger?

Abstract

Contributions in the Special Issue The special issue assembles papers centring around log data analysis, natural language processing, and machine learning used to advance educational assessment. They demonstrate how semi‐ and unstructured data such as log and text data can, despite their challenging nature, be handled appropriately to benefit educational assessment. In this editorial, we contextualize the special issue's contributions within the diverse field of modern technology‐based assessments. Reflection on Terminology Moreover, we raise concerns about nowadays' use of the term artificial intelligence (AI) in scientific communication. While the contribution of AI to scientific progress is indisputable, the mere use of methods that have evolved within AI research does not necessarily render tools or studies AI‐related. We argue that academics have the social responsibility to adopt accurate terminology, given it is integral to scientific rigour and proper scientific communication. Implications In view of the inflationary use of the term AI in science, we propose a scheme to locate one's research in the field by focusing on (1) the type of data, (2) the processing involved, and (3) the output of a study and the actions derived from it, which are situated within the (4) scope of a study.

Integrating deep learning techniques for personalized learning pathways in higher education

“…The quantitative results revealed a notable improvement in the learning outcomes of students who used the AI platform. This aligns with previous research suggesting the potential of AI to enhance educational experiences [ 45 ]. The statistical significance observed in the improvement of grades and engagement metrics in the experimental group underscores the efficacy of personalized and adaptive learning approaches.…”

Exploring an effective automated grading model with reliability detection for large‐scale online peer assessment

“…Technically, there is a wealth of semantic information available from peer grades and textual feedback (Uto & Okano, 2021), providing an opportunity to utilize AI as an effective solution for detecting reliability. AI is a broad concept that encompasses traditional machine learning, deep learning, emerging generative AI, and more (Zehner & Hahnel, 2023). Previous studies have successfully applied machine learning and deep learning to examine the peer assessment reliability in specific aspects (Rico‐Juan et al, 2019).…”

Evolving landscape of artificial intelligence (AI) and assessment in education: A bibliometric analysis

“…Advancing from machine learning (ML) to deep learning and ultimately to applied AI (Hassanien et al, 2020), artificial intelligence (AI) refers to the emulation of human cognitive processes, including tasks such as language translation, speech recognition, visual perception, and virtual decision-making, performed by robots and machines (Braiki et al, 2020). These cutting-edge technologies play a pivotal role in reshaping the methods and capabilities of assessment, introducing more sophisticated and nuanced approaches that align with the dynamic nature of the educational landscape (Gardner et al, 2021;Qu et al, 2022;Zehner & Hahnel, 2023). For example, by automatically creating assessments, evaluating students' written constructed responses or essays, and offering guidance and educational materials, natural language processing systems such as ChatGPT can enhance the effectiveness and efficiency of science education (Zhai, 2023).…”