Artificial intelligence has been attracting the attention of educational researchers recently, especially ChatGPT as a generative artificial intelligence tool. The context of generative artificial intelligence could impact different aspects of students’ learning, such as the motivational aspect. The present research intended to investigate the characteristics of students’ task motivation in the artificial intelligence context, specifically in the ChatGPT context. The researchers interviewed 15 students about their experiences with ChatGPT to collect data. The researchers used inductive and deductive content analysis to investigate students’ motivation when learning with ChatGPT. To arrive at the categories and sub-categories of students’ motivation, the researchers used the MAXQDA 2022. Five main categories emerged: task enjoyment, reported effort, result assessment, perceived relevance, and interaction. Each category comprised at least two sub-categories, and each sub-category was further organized into codes. The results indicated more positive characteristics of motivation than negative ones. The previous results could be due to the conversational or social aspect of the chatbot, enabling relationships with humans and enabling the maintenance of good quality conversations with them. We conclude that a generative AI could be utilized in educational settings to promote students’ motivation to learn and thus raise their learning achievement.

This research aimed to develop and validate a rubric to assess Artificial Intelligence (AI) chatbots' effectiveness in accomplishing tasks, particularly within educational contexts. Given the rapidly growing integration of AI in various sectors, including education, a systematic and robust tool for evaluating AI chatbot performance is essential. This investigation involved a rigorous process including expert involvement to ensure content validity, as well as the application of statistical tests for assessing internal consistency and reliability. Factor analysis also revealed two significant domains, "Quality of Content" and "Quality of Expression", which further enhanced the construct validity of the evaluation scale. The results from this investigation robustly affirm the reliability and validity of the developed rubric, thus marking a significant advancement in the sphere of AI chatbot performance evaluation within educational contexts. Nonetheless, the study simultaneously emphasizes the requirement for additional validation research, specifically those entailing a variety of tasks and diverse AI chatbots, to further corroborate these findings. The ramifications of this research are profound, offering both researchers and practitioners engaged in chatbot development and evaluation a comprehensive and validated framework for the assessment of chatbot performance.

This research presents a novel model, AIEd Bloom’s taxonomy, which integrates artificial intelligence (AI) technologies with Bloom’s taxonomy for learning objectives. The model comprises six levels: collect, adapt, simulate, process, evaluate, and innovate. Each level utilizes AI-powered platforms and tools to enhance the teaching and learning processes, such as organizing resources, creating adaptive learning experiences, simulating experiential learning, visualizing data for enhanced comprehension, and the providing evaluation tools for both formative and summative assessment. The proposed model aims to augment both the efficiency and effectiveness of educational processes in the context of the digital age. Designed for application within educational settings, this model has the potential to enhance learning through a variety of methods. Furthermore, its utility extends across a diverse range of cognitive levels, enabling the achievement of multiple learning objectives. This research constitutes a significant contribution to the burgeoning field of AI in education (AIEd) by presenting a cohesive framework that strategically aligns AI functionalities with Bloom’s taxonomy, thus enriching the overall educational ecosystem.

With Extended Reality (XR), it is possible to enhance our real-world experiences through a fusion of immersive and interactive technologies. In the present research, the researchers intended to study high school students’ engagement in Biology learning in the context of XR. Fourteen tenth-grade students who learned the biological topics of the cell and the heart participated in the research. During the selection process, consideration was given to the students’ readiness to participate in the program. The researchers collected data via interviews and observations. The interview questions were related to the four types of engagement: The behavioral, the cognitive, the emotional, and the social. The observations were used to triangulate the interview-based data. To analyze the data, the researchers used deductive and inductive content analysis. The research results indicated the XR context encouraged the participants to engage in four types of engagement: cognitive engagement (learning perception, learning assessment, learning regulation, learning application), emotional engagement (learning sufficiency, affection for learning, and learning motivation), social engagement (interaction and communication), and behavioral engagement (achievement and good classroom behavior). The observations results supported the results from the interviews.

Emerging educational technologies such as Artificial Intelligence (AI) and Extended Reality (XR) promise to transform traditional classrooms into dynamic, personalized, and immersive learning environments. Yet, the potential of combining these innovative technologies and understanding their nuanced impacts on learners remains underexplored. This study addresses this gap by examining how AI, XR, and a combined AI-XR approach influence student satisfaction, mediated by student engagement and moderated by individual learner characteristics. Utilizing a cluster-randomized experimental design with 888 high school students, the research compares traditional teaching methods against AI-enhanced, XR-enhanced, and combined AI-XR instructional settings. Grounded in Kolb’s Experiential Learning Theory and Self-Determination Theory, findings reveal that student engagement significantly mediates the relationship between advanced learning environments and student satisfaction, with the combined AI-XR condition demonstrating the most potent effects. Further analysis highlights the moderating role of learner characteristics: students with high technological proficiency and male students show heightened engagement and satisfaction in immersive XR-based contexts, whereas AI-based personalized support notably benefits learners with lower initial motivation. Self-efficacy showed no significant moderating effect. The study underscores engagement as a critical mechanism for achieving positive outcomes through innovative educational technology and emphasizes the importance of designing instructional experiences that account for diverse learner profiles. The research offers valuable in-sights for educators and instructional designers seeking to harness AI and XR technologies effectively to enhance learner experiences in secondary education.