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Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline the key aspects of designing, implementing and evaluating these agents. The data sources were empirical studies, including peer-reviewed conference papers and journal articles, and the most recent publications, from the ACM Digital Library, IEEE Xplore, ProQuest, ScienceDirect, Scopus, SpringerLink, Taylor & Francis Online, Web of Science and Wiley Online Library. The remaining papers underwent a rigorous quality assessment. A filter study meeting the objective was based on keywords. Comparative analysis and synthesis of results were used to handle data and combine study outcomes. Out of 1162 search results, 13 studies were selected. The results indicate that agents promote dialogic learning, proficiency in knowledge domains, personalized feedback and empathic abilities as essential design principles. Most implementations employ a quantitative approach, and two variables are used for evaluation. Feedback types play a vital role in achieving positive results in learning performance and student perceptions. The main limitations and gaps are the time range for literature selection, the level of integration of the empathic field and the lack of a detailed development stage report. Moreover, future directions are the ethical implications of agents operating beyond scheduled learning times and the adoption of Responsible AI principles. In conclusion, this review provides a comprehensive framework of empathic PCAs, mostly in their evaluation. The systematic review registration number is osf.io/3xk6a.

Artificial intelligence (AI) and natural language processing technologies have fuelled the growth of Pedagogical Conversational Agents (PCAs) with empathic conversational capabilities. However, no systematic literature review has explored the intersection between conversational agents, education and emotion. Therefore, this study aimed to outline the key aspects of designing, implementing and evaluating these agents. The data sources were empirical studies, including peer‐reviewed conference papers and journal articles, and the most recent publications, from the ACM Digital Library, IEEE Xplore, ProQuest, ScienceDirect, Scopus, SpringerLink, Taylor & Francis Online, Web of Science and Wiley Online Library. The remaining papers underwent a rigorous quality assessment. A filter study meeting the objective was based on keywords. Comparative analysis and synthesis of results were used to handle data and combine study outcomes. Out of 1162 search results, 13 studies were selected. The results indicate that agents promote dialogic learning, proficiency in knowledge domains, personalized feedback and empathic abilities as essential design principles. Most implementations employ a quantitative approach, and two variables are used for evaluation. Feedback types play a vital role in achieving positive results in learning performance and student perceptions. The main limitations and gaps are the time range for literature selection, the level of integration of the empathic field and the lack of a detailed development stage report. Moreover, future directions are the ethical implications of agents operating beyond scheduled learning times and the adoption of Responsible AI principles. In conclusion, this review provides a comprehensive framework of empathic PCAs, mostly in their evaluation. The systematic review registration number is osf.io/3xk6a.Practitioner notesWhat is already known about this topicEmotions play a pivotal role in shaping the interaction process, making it essential to consider them when designing methodological strategies or learning tools.Empathic Pedagogical Conversational Agents (PCAs) have emerged as a crucial approach for enhancing and personalizing the learning experience (24/7) for pupils and supporting human teachers in their teaching process.Despite the creation of numerous empathic PCAs, there is a scarcity of Systematic Literature Reviews (SLRs) on their application in the educational field, particularly concerning the integration of emotional abilities in combination with the competencies of each subject.What this paper addsIt offers new insights into the design principles underlying the integration of the empathic field.It reviews different approaches for incorporating students' prior knowledge in real time.It provides a comprehensive and up‐to‐date overview of the research designs used for implementation, including quantitative, qualitative and mixed methods.It examines the factors that influence the effectiveness of empathic PCA in teaching and learning.It evaluates the types of feedback that enhance the impact of the empathic field on learning outcomes.Implications for practice and/or policyIt is crucial to grasp the topics that this paper introduces in order to effectively integrate new learning tools into any context.Techno‐pedagogical designers seeking to gain insights into empathic PCAs will find immense value in this SLR, as it comprehensively covers each stage of the process.For future research endeavours, this study offers a wealth of ideas to draw upon, enabling researchers to address the challenges outlined and explore new avenues of investigation. [ABSTRACT FROM AUTHOR]

In recent years, AI technologies have been developed to promote students' self-regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI-based (SRLbot) and rule-based AI chatbots (Nemobot) in a 3-week science learning experience with 74 Secondary 4 students in Hong Kong. The experimental group used SRLbot to maintain a regular study habit and facilitate their SRL, while the control group utilized rule-based AI chatbots. Results showed that SRLbot effectively enhanced students' science knowledge, behavioural engagement and motivation. Quantile regression analysis indicated that the number of interactions significantly predicted variations in SRL. Students appreciated the personalized recommendations and flexibility of SRLbot, which adjusted responses based on their specific learning and SRL scenarios. The ChatGPT-enhanced instructional design reduced learning anxiety and promoted learning performance, motivation and sustained learning habits. Students' feedback on learning challenges, psychological support and self-regulation behaviours provided insights into their progress and experience with this technology. SRLbot's adaptability and personalized approach distinguished it from rule-based chatbots. The findings offer valuable evidence for AI developers and educators to consider generative AI settings and chatbot design, facilitating greater success in online science learning.

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs-based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer-reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state-of-the-art models (eg, GPT-3/4), embracing the initiative of open-sourcing models/systems, and adopting a human-centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models.

This paper presents a multidimensional view of AI's role in education, emphasising the intricate interplay among AI, analytics and human learning processes. Here, I challenge the prevalent narrow conceptualisation of AI as tools in Education, exemplified in generative AI tools, and argue for the importance of alternative conceptualisations of AI for achieving human–AI hybrid intelligence. I highlight the differences between human intelligence and artificial information processing, the importance of hybrid human–AI systems to extend human cognition and posit that AI can also serve as an instrument for understanding human learning. Early learning sciences and AI in Education Research (AIED), which saw AI as an analogy for human intelligence, have diverged from this perspective, prompting a need to rekindle this connection. The paper presents three unique conceptualisations of AI: the externalisation of human cognition, the internalisation of AI models to influence human mental models and the extension of human cognition via tightly coupled human–AI hybrid intelligence systems. Examples from current research and practice are examined as instances of the three conceptualisations in education, highlighting the potential value and limitations of each conceptualisation for human competence development, as well as the perils of overemphasis on approaches that replace human learning opportunities with AI tools. The paper concludes with advocacy for a broader approach to AIED that goes beyond considerations on the design and development of AI and includes educating people about AI and innovating educational systems to remain relevant in an AI ubiquitous world. [ABSTRACT FROM AUTHOR]

Conversational user interfaces (CUI), including voice interfaces, which allow users to converse with computers via voice, are gaining wide popularity. VoiceBots allow users to receive a response in real‐time, regardless of the communication device. VoiceBots have been explored in fields such as customer service to automate repetitive queries and help reduce redundant tasks; however, they have not been widely applied in the classroom. This study aimed to explore the effects of VoiceBot implementation on student learning. A pre‐test–post‐test design was implemented with 65 participating undergraduate students in multimedia programming who were randomly allocated to scenarios representing a 2 × 2 design (experimental and control cohorts). Data were collected using an academic achievement test and semi‐structured interviews, which allowed for a more in‐depth analysis of the students' experiences with the VoiceBot. The results showed that how the VoiceBot was applied positively influenced student learning in the experimental cohort. Moreover, the focus group data demonstrated that the VoiceBot can be a valuable assistant for students and could be easily replicated in other courses. To the best of our knowledge, this study was the first to use VoiceBot to engage undergraduate students in Ghana, thus contributing to the growing literature stream on the development of VoiceBots to improve student learning experiences. This study elucidates the design process using a zero‐coding technique, which is considered a suitable approach for educational institutions with limited resources. Practitioner notes What is already known about this topic Conversational user interfaces (CUIs), including voice interfaces, have gained popularity and are used to interact with computers through natural language. VoiceBots have been utilised in various fields such as customer service to automate tasks and reduce redundancy. Instant messaging systems such as WhatsApp and Telegram have been used for communication in educational contexts. Advances in artificial intelligence (AI) and natural language processing (NLP) have led to significant improvements in voice‐enabled CUIs (VoiceBots). Existing studies indicate that chatbots affect students' motivation, learning experiences, and achievements; however, research on using VoiceBots for learning improvement is limited. What this paper adds A VoiceBot was introduced as an assistant to facilitate learning in a multimedia programming course. The study used an experimental design with an experimental cohort using a WhatsApp group platform equipped with a zero‐coding VoiceBot and a control cohort without the bot. The study found that students interacting with VoiceBot demonstrated better learning achievement than the control group. The study also provides clear suggestions on integrating VoiceBots into educational institutions. Implications for practice and/or policy The study's findings suggest that VoiceBots can play a significant role in improving student learning achievements, especially in subjects such as multimedia programming. Educational institutions could establish learning design and technology centres with subject matter experts to integrate VoiceBots effectively into the learning process. Instructors must possess adequate technological proficiency to engage students with VoiceBots and targeted in‐service training may be necessary. Future research can explore VoiceBot use across various academic domains and levels of education, analyse the impact of usage patterns on learning outcomes, and assess its long‐term effects on student engagement and motivation. What is already known about this topic Conversational user interfaces (CUIs), including voice interfaces, have gained popularity and are used to interact with computers through natural language. VoiceBots have been utilised in various fields such as customer service to automate tasks and reduce redundancy. Instant messaging systems such as WhatsApp and Telegram have been used for communication in educational contexts. Advances in artificial intelligence (AI) and natural language processing (NLP) have led to significant improvements in voice‐enabled CUIs (VoiceBots). Existing studies indicate that chatbots affect students' motivation, learning experiences, and achievements; however, research on using VoiceBots for learning improvement is limited. What this paper adds A VoiceBot was introduced as an assistant to facilitate learning in a multimedia programming course. The study used an experimental design with an experimental cohort using a WhatsApp group platform equipped with a zero‐coding VoiceBot and a control cohort without the bot. The study found that students interacting with VoiceBot demonstrated better learning achievement than the control group. The study also provides clear suggestions on integrating VoiceBots into educational institutions. Implications for practice and/or policy The study's findings suggest that VoiceBots can play a significant role in improving student learning achievements, especially in subjects such as multimedia programming. Educational institutions could establish learning design and technology centres with subject matter experts to integrate VoiceBots effectively into the learning process. Instructors must possess adequate technological proficiency to engage students with VoiceBots and targeted in‐service training may be necessary. Future research can explore VoiceBot use across various academic domains and levels of education, analyse the impact of usage patterns on learning outcomes, and assess its long‐term effects on student engagement and motivation. [ABSTRACT FROM AUTHOR]

A core focus of self‐regulated learning (SRL) research lies in uncovering methods to empower learners within digital learning environments. As digital technologies continue to evolve during the current hype of artificial intelligence (AI) in education, the theoretical, empirical and methodological nuances to support SRL are emerging and offering new ways for adaptive support and guidance for learners. Such affordances offer a unique opportunity for personalised learning experiences, including adaptive interventions. Exploring the application of adaptivity to enhance SRL is an important and emerging area of research that requires further attention. This editorial introduces the contributions of seven papers for the special section on adaptive support for SRL within digital learning environments. These papers explore various themes related to enhancing SRL strategies through technological interventions, offering valuable insights and paving the way for future advancements in this dynamic area. [ABSTRACT FROM AUTHOR]

Training to run is not straightforward since without proper personalised supervision and planning, people will not improve their performance and will increase the risk of injuries. This study aims to identify the different factors that influence running training programmes, examining the benefits, challenges or limitations of personalised plans. Moreover, this study explores how multimodal, immersive and artificial intelligence technologies can support personalised training. We conducted an exploratory sequential mixed research consisting of interviews with 11 running coaches from different countries and a survey of 12 running coaches. Based on the interviews and survey analysis, we identified and extracted relevant factors of the training process. We identified four relevant aspects for running training: physical, technical, mental and body awareness. Using these aspects as a reference, we derived a framework using a bottom‐up approach. This framework proposes multimodal, immersive and artificial intelligence technologies to facilitate personalised running training. It also allows coaches to personally guide their athletes on each aspect. Practitioner notesWhat is already known about this topic Running is a popular sport that provides health benefits and is practised by many people around the world.Training is a process that enables athletes to improve their development in various aspects of their sport; in the case of running, it helps them to increase their speed and endurance.Personalised training supports the needs and abilities of athletes, by helping them to achieve their potential through individualised activities or programmes.Sports science research indicates that personalised training can be improved by applying technology to tackle its challenges and limitations.What this paper adds We show that personalising the training requires not only focusing on the runners' physical condition but also on their mental, technical and body awareness aspects, where each of them has a different adaptation to training.We show that multimodal and immersive technologies offer suitable and portable ways to measure and target the mental and body awareness aspects during running training.Implications for practice and/or policy This paper presents a list of factors, measures and devices that coaches can use to plan and design their training sessions in a more personalised manner.This study can serve as a foundation for future research that aims to identify and target the various factors that influence the learning and training of sports. [ABSTRACT FROM AUTHOR]

Previous research showed that the parents acknowledged the technology's benefits for their young children's learning, however, they are still worried about the extended screen time, lack of physical activity and lack of social interactions. To address these concerns, we developed Kid Space to enable pedagogically appropriate technology use for children in early childhood education by combining various sensing technologies with a multi‐modal conversational artificial intelligence system that can interact with children, understand individual progress and provide personalised learning experiences. To understand the impact of Kid Space on the parents' initial concerns about technology use by their young children, we conducted a multi‐method user study: (1) a quasi‐experimental design and (2) formative research method using an exploratory case study with a set of children and their parents experiencing Kid Space in their homes. The results show that after experiencing Kid Space with their children, the parents felt significantly less concerned about screen time, social interactions and physical activity and reported positive perceptions towards pedagogical value of Kid Space. Detailed analysis on the multi‐modal data quantitatively and qualitatively validated why Kid Space alleviated these concerns. Future research is needed to validate long‐term educational value of Kid Space and generate insights for improvement for next iterations. Practitioner notesWhat is already known about this topic Play‐based learning is critical for young children's education, but digital games create major concerns around extended screen time, lack of physical activity and lack of social interactions.Blending digital and physical spaces could support pedagogically appropriate technology use for young children. Towards this end, there are some exemplary studies in the state–of‐the‐art reporting positive educational outcomes as an effect of utilising such spaces. However, none of these studies supported children's most natural mode of communication in their interactions with the systems—speaking.Pedagogical conversational agents (PCAs) are promising, but they are tricky when it comes to young children's speech because of unique technical challenges resulted from how children use language and communicate with digital systems.What this paper adds To our best knowledge, Kid Space is one of the earliest implementations of a PCA with a multi‐modal artificial intelligence (AI) system utilising physical and digital learning manipulatives for maths learning with a focus on early childhood education. The key contributions of this paper are (1) the design and development of an end‐to‐end multi‐modal system enabling Wizard‐of‐Oz experimentation for initial evaluations with users, (2) the creation of a multi‐modal, in‐the‐wild labelled dataset with children–agent, children–parent and children–physical/digital space interactions enabling advancements for AI components for later evaluations with users and (3) the generation of rich insights from an initial research study on user perceptions and engagement as well as actionable findings to improve Kid Space experiences for next iterations and inform key design features for similar systems.Implications for practice and/or policy The results of the study implied a set of areas for improvement—or design features—for Kid Space and other similar pedagogical conversational systems developed for children's home usages: (1) easier setup and usage with optimised setup size addressing diverse space limitations at homes, (2) minimised latency between Oscar (the conversational pedagogical agent) and child interactions (eg, adding multimodal dialogue system to reduce the need for a human wizard), (3) more advanced personalisation, social (including more verbal interactions) and pedagogical skills for Oscar with increased contextual awareness (eg, sending children's engagement), (4) scalability and higher visual quality of content with diverse games and learning outcomes, (5) parental control features over Kid Space platform and Oscar (eg, time limit, content, etc.) and (6) accessibility features (eg, captions turned on for multilingual children) and support for neurodiversity. [ABSTRACT FROM AUTHOR]

In the ever‐evolving AI‐driven education, integrating AI technologies into teaching practices has become increasingly imperative for aspiring STEM educators. Yet, there remains a dearth of studies exploring pre‐service STEM teachers' readiness to incorporate AI into their teaching practices. This study examined the factors influencing teachers' willingness to integrate AI (WIAI), especially from the perspective of pre‐service STEM teachers' attitudes towards the application of AI in teaching. In the study, a comprehensive survey was conducted among 239 pre‐service STEM teachers, examining the influences and interconnectedness of Technological Pedagogical Content Knowledge (TPACK), Perceived Usefulness (PU), Perceived Ease of Use (PE), and Self‐Efficacy (SE) on WIAI. Structural Equation Modeling (SEM) was employed for data analysis. The findings illuminated direct influences of TPACK, PU, PE, and SE on WIAI. TPACK was found to directly affect PE, PU, and SE, while PE and PU also directly influenced SE. Further analysis revealed significant mediating roles of PE, PU, and SE in the relationship between TPACK and WIAI, highlighting the presence of a chain mediation effect. In light of these insights, the study offers several recommendations on promoting pre‐service STEM teachers' willingness to integrate AI into their teaching practices. Practitioner notes What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. What is already known about this topic? The potential of AI technologies to enrich learning experiences and improve outcomes in STEM education has been recognized. Pre‐service teachers' willingness to integrate AI into teaching practice is crucial for shaping the future learning environment. The TAM and TPACK frameworks are used to analyse teacher factors in technology‐supported learning environments. Few studies have been conducted for examining factors of pre‐service teachers' willingness to integrate AI into teaching practices in the context of STEM education. What this paper adds? A survey was designed and developed for exploring pre‐service STEM teachers' WIAI and its relationships with factors including TPACK, PE, PU, and SE. TPACK, SE, PU, and PE have direct impact on pre‐service STEM teachers' WIAI. SE, PU, and PE have been identified as mediating variables in the relationship between TPACK and WIAI. Two sequential mediation effects, TPACK → PE → SE → WIAI and TPACK → PU → SE → WIAI, among pre‐service STEM teachers were further identified. Implications of this study for practice and/or policy Pre‐service STEM teachers are encouraged to explore and utilize AI technology to enhance their confidence and self‐efficacy in integrating AI into teaching practices. Showcasing successful cases and practical experiences is essential for fostering awareness of AI integration in STEM education. It is recommended to introduce AI education courses in teacher training programs. Offering internship and practicum opportunities related to AI technologies can enhance their practical skills in integrating AI into education. [ABSTRACT FROM AUTHOR]

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks.Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks.Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring.What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation.A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks.Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts.Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks.The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved.Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education. [ABSTRACT FROM AUTHOR]

Scaffolds that support self‐regulated learning (SRL) have been found to improve learning outcomes. The effects of scaffolds can differ depending on how learners use them and how specific scaffolds might influence learning processes differently. Personalized scaffolds have been proposed to be more beneficial for learning due to their adaptivity to learning progress and individualized content to learning needs. The present study investigated finer‐grained effects of how personalized scaffolds driven by a rule‐based artificial intelligence system influenced SRL processes, especially how students learned with them. Using a pre‐post experimental design, we investigated personalized scaffolds based on university students' real‐time learning processes in a technologically enhanced learning environment. Students in the experimental group (n = 30) received personalized scaffolds, while the control group (n = 29) learned without scaffolds. All students completed a 45‐minute learning task with trace data recorded. Findings indicated scaffold effects on students' subsequent learning behaviour. Additionally, only scaffold interaction correlated to essay performance and suggests that the increase in frequencies of SRL activities alone does not contribute directly to learning outcomes. As guidelines for real‐time SRL support are lacking, this study provides valuable insights to enhance SRL support with adaptive learning technologies. Practitioner notesWhat is already known about this topic Self‐regulated learning scaffolds, especially adaptive scaffolds, improve learning.Personalized scaffolds have effects on self‐regulated learning activities.Past research focused on aggregated effects of scaffolds.What this paper adds Investigates how students learn with personalized scaffolds in terms of frequencies of learning activities and scaffold interaction.Takes a closer look at which learning activities and when the effects of personalized scaffolds occur.Examines how finer‐grained effects of personalized scaffolds correspond to learning outcomes.Implications for practice and/or policy Personalized scaffold effects vary across learning, and future research should consider finer‐grained investigations of SRL support in order to better understand their influence on learning.The number of personalized scaffolds provided should be reconsidered in the future as students only use some of the support provided, especially when task demands increase.Personalized scaffold interaction is linked to improvement in task performance, so future research should also focus on students' appropriate use of self‐regulated learning support. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy.Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy.Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes.What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions.Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy.Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy.Practitioners are able to use the AILQ to assess students' AI literacy development. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called Pair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with Pair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems. Practitioner notesWhat is already known about this topic: Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways.Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it.What this paper adds: Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning.Describes how we adapted six design methods to design AI features.Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning.Implications for practice: We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy.Our work also includes a reflection on our design process and implications for future design processes. [ABSTRACT FROM AUTHOR]

While AI has become more prevalent in our society than ever, many young learners are found holding various naive, erroneous conceptions of AI due to the influence of their technology and media environments. To address this issue, this study seeks to propose a novel pedagogical solution to improve upper‐elementary school students' scientific understanding of AI. Following a theory‐informed design convention, we propose an embodied, analogical and disruptive (EAD) approach which is centred on a human–AI comparison through analogical teaching and embodied interaction. To evaluate the impact of this approach, a matched‐group experimental study with pre‐ and posttest interviews was conducted among 77 Grade 6 elementary students in China. The statistical analysis showed that the experimental group, learning via the EAD approach, significantly outperformed their counterparts receiving direct instruction in terms of student growth in understanding. Qualitative analyses revealed the strengths of the EAD approach in supporting student engagement, abstract thinking and system thinking, and its limitations in cognitive overload and communication issues. Patterns of student learning were identified, including their mental schemas and strategies. The EAD approach, as an evidence‐based, age‐appropriate pedagogical solution, demonstrates the value of embodied cognition and human–AI analogy in AI education in elementary education. Practitioner notesWhat is already known about this topic Many young students hold naive, erroneous conceptions about AI.Pedagogical design has long been a critical challenge in teaching AI in K‐12 schools.Human–AI analogies are often used to teach AI, but with obvious pitfalls to be fixed.What this paper adds An embodied, analogical and disruptive (EAD) approach is proposed to improve students' understandings of AI.An experimental study shows statistically significant greater gains in students' understanding with the EAD approach than with the conventional AI‐only direct instruction approach.Qualitative analysis shows the strength of the EAD approach in supporting student engagement, abstract thinking and systems thinking, and its limitations in cognitive overload and communication issues.Implications for practice and/or policy The EAD approach offers a novel pedagogical solution to promote inclusive and quality AI education for young learners.The EAD approach highlights the pedagogical value of embodied cognition and human–AI analogy in AI education.The EAD approach can complement programming and other plugged activities in AI, and more broadly technology and engineering education. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) chatbots are gaining increasing popularity in education. Due to their increasing popularity, many empirical studies have been devoted to exploring the effects of AI chatbots on students' learning outcomes. The proliferation of experimental studies has highlighted the need to summarize and synthesize the inconsistent findings about the effects of AI chatbots on students' learning outcomes. However, few reviews focused on the meta‐analysis of the effects of AI chatbots on students' learning outcomes. The present study performed a meta‐analysis of 24 randomized studies utilizing Stata software (version 14). The main goal of the current study was to meta‐analytically examine the effects of AI chatbots on students' learning outcomes and the moderating effects of educational levels and intervention duration. The results indicated that AI chatbots had a large effect on students' learning outcomes. Moreover, AI chatbots had a greater effect on students in higher education, compared to those in primary education and secondary education. In addition, short interventions were found to have a stronger effect on students' learning outcomes than long interventions. It could be explained by the argument that the novelty effects of AI chatbots could improve learning outcomes in short interventions, but it has worn off in the long interventions. Future designers and educators should make attempt to increase students' learning outcomes by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligence. Practitioner notesWhat is already known about this topicIn recent years, artificial intelligence (AI) chatbots have been gaining increasing popularity in education.Studies undertaken so far have provided conflicting evidence concerning the effects of AI chatbots on students' learning outcomes.There has remained a paucity of meta‐analyses synthesizing the contradictory findings about the effects of AI chatbots on students' learning outcomes.What this paper addsThis study, through meta‐analysis, synthesized these recent findings about the effects of AI chatbots on students' learning outcomes.This study found that AI chatbots could have a large effect on students' learning outcomes.This study found that the effects of AI chatbots were moderated by educational levels and intervention duration.Implications for practice and/or policyAI chatbot designers could make AI chatbots better by equipping AI chatbots with human‐like avatars, gamification elements and emotional intelligencePractitioners and/or teachers should draw attention to the positive and negative effects of AI chatbots on students.Considering the importance of ChatGPT, more research is required to develop a better understanding of the effects of ChatGPT in education.More research is needed to examine the mechanisms underlying the effects of AI chatbots on students' learning outcomes. [ABSTRACT FROM AUTHOR]