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The majority of AI literacy studies have designed and developed self-reported questionnaires to assess AI learning and understanding. These studies assessed students' perceived AI capability rather than AI literacy because self-perceptions are seldom an accurate account of true measures. International assessment programs that use objective measures to assess science, mathematical, digital, and computational literacy back up this argument. Furthermore, because AI education research is still in its infancy, the current definition of AI literacy in the literature may not meet the needs of young students. Therefore, this study aims to develop and validate an AI literacy test for school students...

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Artificial intelligence (AI) adoption in education requires strategic leadership from education managers, yet little research examines AI readiness among this stakeholder group, particularly in post-Soviet transitional contexts. As decision-makers who control whether and how AI enters educational practice, managers’ readiness is essential for evidence-based policy. This study investigates (1) the current state of AI adoption readiness among Ukrainian education managers, including the gap between personal and institutional readiness; (2) the barriers and facilitators that most strongly influence AI readiness; (3) distinct manager typologies based on readiness profiles; and (4) policy...

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This study explores the integration of generative AI into English as a Foreign Language (EFL) teaching preparation within Indonesian higher education, addressing the growing need to understand how emerging technologies can enhance pedagogical practices in a rapidly evolving educational landscape. By employing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Activity Theory, the research provides a robust analytical framework to examine the factors influencing lecturers' adoption of generative AI. The study is particularly relevant as generative AI offers significant potential to improve teaching efficiency and content personalization, yet its adoption presents challenges...

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Conversational agents such as chatbots have recently gained a lot of traction in various industries, including education, marketing, and healthcare. In K–12 and higher education, conversational agents have been used to boost student learning and motivation. Recently, new applications of conversational agents, such as conversation-based assessments, have emerged in the context of educational assessments. Such applications aim to enhance students' assessment experiences through the use of conversational agents in administering items (or tasks) and providing feedback to students. To discuss the utility of conversational agents in assessments, this paper puts a special emphasis on test-...

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Self-regulated learning (SRL) encapsulates learners' abilities to control, monitor, and regulate cognitive and metacognitive processes such as motivation, emotion, and learning strategies. A crucial element of SRL proficiency lies in learners' capacity to plan and implement effective learning strategies. As such, extensive academic research has been devoted to understanding the impact of learning strategies on learning performance, and the role of instructional methods (including real-time scaffolding) in amplifying students' ability to adopt effective learning strategies. However, while numerous studies have focused on learning strategies and SRL scaffolding, few have investigated their...

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Contract cheating, a form of academic misconduct in which students outsource assessment activities to third parties, is a topic of concern among educators. As similarity-detection systems are ineffective at detecting contract cheating, some institutions have turned to intensely criticised proctoring systems, however student and educator bodies report high costs and privacy concerns. Oral assessment is an alternative assessment approach that provides valuable interpersonal and communication skills in graduates and can naturally help detect and deter cheating. However, oral assessment is typically time-consuming, and in larger courses, it is challenging to validate respondents' identity....

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Artificial Intelligence (AI) and Computer Vision (CV) have rapidly permeated various industries, increasing demand for professionals well-versed in these disciplines. In response to this need, this study evaluates the effectiveness of a novel undergraduate course that aims to equip students with foundational AI and CV knowledge and skills. Unlike existing software-centric courses, this class capitalizes on drones as a practical application area for AI and CV, fostering an active learning environment. The course was conducted over five semesters at a U.S. research institution, with 153 participating students. Data were collected using diverse methods, including formative and summative...

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Teacher corrective feedback (TCF) plays a vital role in second language (L2) learning. Recent studies have examined feedback provided by both human teachers and large language models (LLMs). However, little is known about how students' trust differs toward scaffolded corrective feedback (SCF)—that is, feedback that incrementally progresses from indirect to direct during interaction—when it is provided by an LLM such as ChatGPT versus a language teacher. To address this gap, this study compared the effects of SCF, delivered by language teachers and ChatGPT, on L2 learning outcomes and student trust. Using a mixed-methods design, 40 lower-intermediate Iranian learners of English as a foreign...

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Assessment is a crucial part of education. Traditional marking is a source of inconsistencies and unconscious bias, placing a high cognitive load on the assessors. One approach to address these issues is comparative judgement (CJ). In CJ, the assessor is presented with a pair of items of work, and asked to select the better one. Following a series of comparisons, a rank for any item may be derived using a ranking model, for example, the Bradley-Terry model, based on the pairwise comparisons. While CJ is considered to be a reliable method for conducting marking, there are concerns surrounding its transparency, and the ideal number of pairwise comparisons to generate a reliable estimation of...

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This qualitative case study examines undergraduate students’ engagement with generative artificial intelligence (GenAI) in academic learning in an English-medium university setting. This study employs self-determination theory (SDT) as the primary interpretive framework, treating technology acceptance perceptions (e.g., usefulness and ease of use) as descriptive cues rather than explanatory constructs. Data from 23 semi-structured interviews were analyzed using reflexive thematic analysis, complemented by epistemic network analysis (ENA) to examine the structural relationships among themes in students’ discourse, with automated coding validated against a manually coded subset. Students...

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