Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
2,177
result(s) for
"Intelligent Tutoring Systems"
Sort by:
Trends, Research Issues and Applications of Artificial Intelligence in Language Education
Artificial Intelligence (AI) plays an increasingly important role in language education; however, the trends, research issues, and applications of AI in language learning remain largely under-investigated. Accordingly, the present paper, using bibliometric analysis, investigates these issues via a review of 516 papers published between 2000 and 2019, focusing on how AI was integrated into language education. Findings revealed that the frequency of studies on AI-enhanced language education increased over the period. The USA and Arizona State University were the most active country and institution, respectively. The 10 most popular topics were: (1) automated writing evaluation; (2) intelligent tutoring systems (ITS) for reading and writing; (3) automated error detection; (4) computer-mediated communication; (5) personalized systems for language learning; (6) natural language and vocabulary learning; (7) web resources and web-based systems for language learning; (8) ITS for writing in English for specific purposes; (9) intelligent tutoring and assessment systems for pronunciation and speech training; and (10) affective states and emotions. The results also indicated that AI was frequently used to assist students in learning writing, reading, vocabulary, grammar, speaking, and listening. Natural language processing, automated speech recognition, and learner profiling were commonly applied to develop automated writing evaluation, personalized learning, and intelligent tutoring systems.
Journal Article
Examining the applications of intelligent tutoring systems in real educational contexts: A systematic literature review from the social experiment perspective
Intelligent Tutoring Systems (ITSs) have a great potential to effectively transform teaching and learning. As more efforts have been put on designing and developing ITSs and integrating them within learning and instruction, mixed types of results about the effectiveness of ITS have been reported. Therefore, it is necessary to investigate how ITSs work in real and natural educational contexts and the associated challenges of ITS application and evaluation. Through a systematic literature review method, this study analyzed 40 qualified studies that applied social experiment methods to examine the effectiveness of ITS during 2011–2022. The obtained results highlighted a complicated landscape regarding the effectiveness of ITS in real educational contexts. Specifically, there was an “intelligent” regional gap regarding the distribution of countries where ITS studies using social experiment methods were conducted. Compared to learning performance, relatively less attention was paid to investigating the impact of ITS on non-cognitive factors, process-oriented factors, and social outcomes, calling for more research in this regard. Considering the complexities and challenges existing in real educational fields, there was a lack of scientific rigor in terms of experimental design and data analysis in some of the studies. Based on these findings, suggestions for future study and implications were proposed.
Journal Article
Understanding secondary students’ continuance intention to adopt AI-powered intelligent tutoring system for English learning
Previous studies have demonstrated the effectiveness of intelligent tutoring systems (ITS) in facilitating English learning. However, no empirical research has been conducted on secondary students’ intention to use ITSs in the language domain. This study proposes an extended technology acceptance model (TAM) to predict secondary students’ continuance intention to use and actual use of ITSs for English learning. The model included fifteen hypotheses that were tested with 528 senior secondary students in China. The results of structural equation modeling showed that (1) perceived usefulness and price value had direct positive impacts on continuance intention; (2) perceived ease of use was not directly associated with students’ intention but indirectly influenced intention via perceived usefulness; (3) through the mediation of perceptions, learning goal orientation and facilitating conditions were positively associated with continuance intention; (4) perceived enjoyment positively predicted and anxiety negatively predicted students’ intention to use ITSs; and (5) students’ continuance intention to use ITSs was significantly positively associated with their actual use of ITSs for English learning. The model showed strong explanatory power and might be implemented in future research. This study contributes to the theory and practice of ITSs in K-12 education.
Journal Article
Enhancing Post secondary Writers' Writing Skills with a Chatbot: A Mixed-Method Classroom Study
In the present study, we developed a chatbot that helps teachers to deliver writing instructions. By working with the chatbot, the post-secondary writers developed a thesis statement for their argumentative essay outlines, and the chatbot helped the writers to refine their peer review feedback. We conducted a preliminary analysis of the effect of a chatbot on these writers' writing achievement. We also collected several student testimonials about their chatbot experiences. Several important pedagogical and research implications for chatbot-guided writing instructions and the use of learning technology have been addressed.
Journal Article
Stupid Tutoring Systems, Intelligent Humans
The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today are much simpler. In this article, I present hypotheses on the factors underlying this development, and discuss the potential of educational data mining driving human decision-making as an alternate paradigm for online learning, focusing on intelligence amplification rather than artificial intelligence.
Journal Article
A complex systems approach to analyzing pedagogical agents’ scaffolding of self-regulated learning within an intelligent tutoring system
Self-regulated learning (SRL), learners’ monitoring and control of cognitive, affective, metacognitive, and motivational processes, is essential for learning. However, cognitive and metacognitive SRL strategies are not typically used accurately leading to poor learning outcomes. Intelligent tutoring systems (ITSs) attempt to address this issue by prompting and scaffolding learners to engage in SRL via using pedagogical agents. However, current literature does not examine the extent to which learners’ deployed strategies are functional or dysfunctional in relation to pedagogical agent scaffolding. The current study collected 117 undergraduate students’ data as they learned with MetaTutor, an ITS about the human circulatory system. Participants were randomly assigned to either the (1) Prompt and Feedback Condition where pedagogical agents scaffolded cognitive and metacognitive SRL strategies or (2) Control Condition where no prompts or feedback were provided. Results demonstrated that learners who received prompts by the pedagogical agents to engage in SRL had higher learning gains as well as greater frequencies across most strategies compared to those in the Control Condition who relied on self-initiated strategy use. While sequential transitions across all strategies were not significant between conditions, further analysis grounded in Complex Systems Theory found that learners who were prompted to engage in strategies demonstrated a significantly lower degree of repetition and balance between repetitive and novel patterns of strategy use. The findings suggest that pedagogical agents within MetaTutor successfully scaffolded the functional deployment of cognitive and metacognitive SRL strategies and are indicative of higher learning after interacting with ITSs.
Journal Article