ChatGPT is an AI tool that has sparked debates about its potential implications for education. We used the SWOT analysis framework to outline ChatGPT’s strengths and weaknesses and to discuss its opportunities for and threats to education. The strengths include using a sophisticated natural language model to generate plausible answers, self-improving capability, and providing personalised and real-time responses. As such, ChatGPT can increase access to information, facilitate personalised and complex learning, and decrease teaching workload, thereby making key processes and tasks more efficient. The weaknesses are a lack of deep understanding, difficulty in evaluating the quality of responses, a risk of bias and discrimination, and a lack of higher-order thinking skills. Threats to education include a lack of understanding of the context, threatening academic integrity, perpetuating discrimination in education, democratising plagiarism, and declining high-order cognitive skills. We provide agenda for educational practice and research in times of ChatGPT.

Recently, ChatGPT, a cutting-edge large language model, has emerged as a powerful Generative Artificial Intelligence (GenAI) tool with the capacity to influence education. ChatGPT provides ample opportunities for learners, researchers, educators, and practitioners to achieve the intended learning outcomes in various disciplines. This special issue examines the diverse applications and implications of GenAI tools including ChatGPT in education, highlighting their potential to enhance teaching and learning across various contexts. Key findings from seventeen studies collected in this special issue demonstrate that GenAI tools can significantly improve educational outcomes by providing personalized feedback, facilitating language learning, and supporting both qualitative and quantitative research methodologies. The findings emphasize GenAI’s capacity to increase learner engagement and motivation, yet also underscore the need for robust ethical guidelines and human oversight due to potential issues with privacy, bias, and accuracy. This special issue also highlights the challenges GenAI faces, such as limitations in contextual understanding and its impact on critical thinking skills. In addition, it provides a foundational framework for exploring effective and responsible GenAI integration, aiming to enrich educational experiences. We conclude that future research should focus on the longitudinal effects of GenAI tools on learning outcomes, developing ethical frameworks for their use, and ensuring their adaptability to diverse learner populations to promote inclusive educational practices.

So far, the synergistic relationship between learning design and learning analytics and its impact on learners’ educational practices have been investigated. However, little work has been done to show how integrated learning design based on constructivism learning theory and learning analytics can affect learners’ engagement and self-regulation. Thus, this study aimed to investigate the effect of the Constructivist Learning Design and Learning Analytics (CLDLA) Model on learners’ engagement and self-regulation. In this experimental study, 50 graduate students at Iranian’ Allameh Tabataba’i University were asked to participate in a Moodle course called “Teaching Skills” and fill out Agentic Engagement Scale and Self-Regulation Questionnaires in two steps as a pre-test and post-test measurements. Analysis of the data indicated that the CLDLA model has a positive impact on learners’ engagement and self-regulation. Based on the results, recommendations for further research and educational practices are suggested.

This study investigates the effects of a developed MOOC based on Merrill’s principles of instruction on participants’ learning outcomes and satisfaction. A pre-test-post-test with a control group design was used in this study. In total, 335 participants were assigned into experimental (using Merrill’s principles of instruction) and control group (using the conventional method). However, 291 subjects (143 participants from the experimental and 148 participants from the control group condition) remained in the course up to the last session, and only 200 participants (100 participants per each condition) filled in the surveys. The results were in favour of participants in the experimental condition both in terms of learning and satisfaction compared to the participants in the control group condition. By implementing Merrill’s principles of instruction in designing MOOCs and actively engaging participants in a problem-centred learning process, their learning outcomes and satisfaction can be further improved.