The mediating effects of needs satisfaction on the relationships between prior knowledge and self‐regulated learning through artificial intelligence chatbot

Xia, Qi; Chiu, Thomas K. F.; Chai, Ching Sing; Xie, Kui

doi:10.1111/bjet.13305

Cited by 41 publications

(31 citation statements)

References 84 publications

(137 reference statements)

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“…SRLbot In RQ3, quantile regression analysis indicates that the number of by students has higher slope estimates compared to gender when using SRLbot. Additionally, the effects of students' interactions on their SRL are significant across all quantiles when utilizing AI chatbots, which aligns with previous studies Xia et al, 2023). Maldonado-Mahauad et al (2022) have suggested a possible explanation for this, stating that increased interaction with generative AI chatbots makes it easier to ascertain their impact on SRL.…”

Section: Benefits Limitationssupporting

confidence: 88%

Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study

Ng,

Tan,

Leung

2024

Brit J Educational Tech

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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. Practitioner notesWhat is already known about this topic AI technologies have been used to support student self‐regulated learning (SRL) across subjects. SRL has been identified as an important aspect of student learning that can be developed through technological support. Generative AI technologies like ChatGPT have shown potential for enhancing student learning by providing personalized guidance and feedback. What this paper adds This paper reports on a case study that specifically examines the effectiveness of ChatGPT in promoting SRL among secondary students. The study provides evidence that ChatGPT can enhance students' science knowledge, motivation and SRL compared to a rule‐based AI chatbot. The study offers insights into how ChatGPT can be used as a tool to facilitate SRL and promote sustained learning habits. Implications for practice and/or policy The findings of this study suggest that educators should consider the potential of ChatGPT and other generative AI technologies to support student learning and SRL. Educators and students should be aware of the limitations of AI technologies and ensure that they are used appropriately to generate desired responses. It is also important to equip teachers and students with AI competencies to enable them to use AI for learning and teaching.

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Section: Benefits Limitationssupporting

confidence: 88%

Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study

Ng,

Tan,

Leung

2024

Brit J Educational Tech

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“…We included these two covariates due to their significant correlation with perception outcomes measures, and thus their inclusion will improve the precision of model estimates. Other prior studies also suggested the role age and prior knowledge played in people's perceptions of AI [55,56]. Results are displayed in Table 5.…”

Section: Effects Of Transparency and Framing On Perceptionmentioning

confidence: 73%

Transparency Enhances Positive Perceptions of Social Artificial Intelligence

Xu,

Bradford,

Garg

2023

Human Behavior and Emerging Technologies

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Social chatbots are aimed at building emotional bonds with users, and thus it is particularly important to design these technologies so as to elicit positive perceptions from users. In the current study, we investigate the impacts that transparent explanations of chatbots’ mechanisms have on users’ perceptions of the chatbots. A total of 914 participants were recruited from Amazon Mechanical Turk. They were randomly assigned to observe conversations between a hypothetical chatbot and a user in one of the two-by-two experimental conditions: whether the participants received an explanation about how the chatbot was trained and whether the chatbot was framed as an intelligent entity or a machine. A fifth group, who believed they were observing interactions between two humans, served as a control. Analyses of participants’ responses to the postobservation survey indicated that transparency positively affected perceptions of social chatbots by leading users to (1) find the chatbot less creepy, (2) feel greater affinity to the chatbot, and (3) perceive the chatbot as more socially intelligent, though these effects were small. Moreover, transparency appeared to have a larger effect on increasing the perceived social intelligence among participants with lower prior AI knowledge. These findings have implications for the design of future social chatbots and support the addition of transparency and explanation for chatbot users.

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“…Third, educational need analysis may help specify the role of artificial intelligence chatbots in particular educational contexts, which demonstrates the perspective of characteristics and functions regarding technologies (Xia et al , 2023 for the effects of need satisfaction). As the clustering results and the existing literature reviews have revealed, chatbots’ functions are currently limited.…”

Section: Discussionmentioning

confidence: 99%

A bibliometric analysis of artificial intelligence chatbots in educational contexts

Yu-peng

2023

ITSE

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Purpose The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots. Design/methodology/approach This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering. Findings Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education. Research limitations/implications The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective. Originality/value This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

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The mediating effects of needs satisfaction on the relationships between prior knowledge and self‐regulated learning through artificial intelligence chatbot

Cited by 41 publications

References 84 publications

Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study

Empowering student self‐regulated learning and science education through ChatGPT: A pioneering pilot study

Transparency Enhances Positive Perceptions of Social Artificial Intelligence

A bibliometric analysis of artificial intelligence chatbots in educational contexts

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