Use Of Intelligent Tutoring Systems Research Articles

Factors affecting Chinese undergraduate medical students’ behavioural intention and actual use of intelligent tutoring systems

This study examined Chinese undergraduate medical students’ acceptance and adoption of intelligent tutoring systems (ITSs) using the general extended technology acceptance model for e-learning via a Likert-scale questionnaire. Specifically, it examined the relations between the five antecedents and the four core components in the model (i.e., perceived usefulness (PU), perceived ease of use (PEOU), behavioural intention and actual use of ITSs). The results of PLS-SEM showed that perceived enjoyment was the most influential antecedent as it significantly impacted both PU and PEOU. Both self-efficacy and prior experience only significantly contributed to PEOU but not PU. Both PU and PEOU significantly and positively predicted behavioural intention, which in turn had a significant and positive path to actual use. The results provide some practical implications to teachers as to how to encourage Chinese undergraduate medical students’ adoption of ITSs: by integrating some gamification elements into the learning activities in ITSs to foster students’ enjoyable feelings or familiarising students with using ITSs so that they can quickly adapt to learning through them. This could be achieved by providing guidance in using ITSs via videos, websites or booklets, or at the beginning of the course, inviting senior students to share their perceived advantages and usefulness of using ITSs. Implications for practice or policy: Because of the importance of perceived enjoyment, teachers may integrate some gamification into the learning activities in ITSs to nurture students’ enjoyable feelings. To enable students to quickly accommodate learning via ITSs, institutions may provide students with information on common features of ITSs or how to navigate a specific ITS. Teachers may explicitly explain how learning objectives can be better achieved through using an ITSs so that students will appreciate its usefulness.

A bibliometric analysis of artificial intelligence in L2 teaching and applied linguistics between 1995 and 2022

Abstract This study offers a comprehensive bibliometric analysis of artificial intelligence (AI) applications in the field of second language (L2) teaching and applied linguistics, spanning from the early developments in 1995 to 2022. It aims to uncover current trends, prominent themes, and influential authors, documents, and sources. A total of 185 relevant articles published in Social Sciences Citation Index (SSCI) indexed journals were analyzed using the VOSviewer bibliometric software tool. Our investigation reveals a highly multidisciplinary and interconnected field, with four main clusters identified: AI, natural language processing (NLP), robot-assisted language learning, and chatbots. Notable themes include the increasing use of intelligent tutoring systems, the importance of syntactic complexity and vocabulary in L2 learning, and the exploration of robots and gamification in language education. The study also highlights the potential of NLP and AI technologies to enhance personalized feedback and instruction for language learners. The findings emphasize the growing interest in AI applications in L2 teaching and applied linguistics, as well as the need for continued research to advance the field and improve language instruction and assessment. By providing a quantitative and rigorous overview of the literature, this study contributes valuable insights into the current state of research in AI-assisted L2 teaching and applied linguistics and identifies key areas for future exploration and development.

Performance increases in mathematics within an intelligent tutoring system during COVID-19 related school closures: a large-scale longitudinal evaluation

The closure of schools due to COVID-19 disrupted learning routines of thousands of students, resulting in reported performance decreases, especially among lower-performing students. However, some studies on students’ performance within intelligent tutoring systems (ITS) also found significant performance increases for times of school closures as compared to before. However, little is known about students’ longitudinal performance trajectories within ITS. Accordingly, we evaluated longitudinal data from German students (n ≈ 2,700 students; n ≈ 5 million problems) enrolled in an ITS for learning mathematics from January 2017 until the end of May 2021 to investigate the effect of periods of school closures (first and second) on students’ performance within the ITS during, between, and after school closures. We observed significant performance increases for both lower- and higher-performing students during, between, and after COVID-19 related school closures. Importantly, these improvements were more pronounced for lower-performing students compared to higher-performing students. Together, these results suggest that ITS may have helped to maintain mathematics learning, particularly for lower-performing students during COVID-19 related school closures and that these beneficial effects persisted at least for the following months when schools opened again. As such, the use of ITS for learning mathematics seems an appropriate approach for distance learning during times of crisis.

Systematic Review: Trends in Intelligent Tutoring Systems in Mathematics Teaching and Learning

The use of intelligent tutoring systems (ITSs) is growing rapidly in the field of education. In mathematics, adaptive and personalized scenarios mediated by these systems have been implemented to aid concept comprehension and skill development. This study presents a systematic review on the current status of the use of ITSs in mathematics education in higher education institutions (HEI). More specifically, it inquires about elements rarely addressed in reviews, such as the purposes, advantages, effectiveness, limitations, characteristics, and development and research methodologies in ITSs used for mathematics education. We analyzed 43 documents in 15 specialized and indexed journals in the years 2012-2022. In addition to providing a global overview of the implementation of ITSs in mathematics teaching and learning, we also present trends and opportunities for future research on building educational environments guided by these technologies.

Towards a multi-modal Deep Learning Architecture for User Modeling

Deep learning has succeeded in various applications, including image classification and feature learning. However, there needs to be more research on its use in Intelligent Tutoring Systems or Serious Games, particularly in modeling user behavior during learning or gaming sessions using multi-modal data. Creating an effective user model is crucial for developing a highly adaptive system. To achieve this, it is necessary to consider all available data sources to inform the user’s current state. This study proposes a user-sensitive deep multi-modal architecture that leverages deep learning and user data to extract a rich latent representation of the user. The architecture combines a Long Short-Term Memory, a Convolutional Neural Network, and multiple Deep Neu-ral Networks to handle the multi-modality of data. The resulting model was evaluated on a public multi-modal dataset, achieving better results than state-of-the-art algorithms for a similar task: opinion polarity detection. These findings suggest that the latent representation learned from the data is useful in discriminating behaviors. This proposed solution can be applied in various contexts where user modeling using multi-modal data is critical for improving the user experience.

Flow with an intelligent tutor: A latent variable modeling approach to tracking flow during artificial tutoring.

Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent process: the (i) latent class model, (ii) latent transition model, and (iii) hidden Markov model. We illustrate application of each model using example log data from Cognitive Tutor Algebra I and suggest analytic procedures of drawing learning flow. Through experimental application, we show that the models can reveal substantive information about students' learning behaviors and have potential utility for describing the learning flow. The models differed in the assumptions and data constraints but yielded consistent findings on the flow states and interaction modalities. Based on our experiential analyses, we discuss strengths and limitations of the models and illuminate areas of future development.

AN INTELLIGENT TUTORING SYSTEM TO MAINTAIN THE STUDENTS’ MOTIVATION

Abstract. Recently, many educational institutions around the world has transformed to online education specially during the COVID-19 pandemic. This fast and in many cases unplanned transformation leads to the needs for more researches to find solutions for the problems of this rapid transformation. As it's the best economic options during this pandemic, this study focused on creating a web-based (asynchronous system) intelligent tutoring system (ITS) to support the teachers in the C programming language course. Nonetheless, the suggested system takes into consideration one of the biggest challenges for asynchronous system which is how to maintain the students' motivation for the entire learning process. Therefore, the current study suggested the use of an interactive ITS as a solution for this challenge. The created system C-ITS used a set of motivational state rules and tactics to assess and maintain the motivation of the students. Finally, after using the system by the students and the teachers for two weeks, we conducted an evaluation study to evaluate the quality of the system design, the usability, the functionality, the compatibility. The result of the evaluation study showed that C-ITS system acceptable from both the students and the teachers.

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.

A Systematic Review of Research on the use and Impact of Technology for Learning Chinese

In light of technological development enforced by the Covid-19 pandemic, learning Chinese has become more digitalised. Confucius institutes went online and now follow 2021 to 2025 Action Plans for the Construction of Teaching Resources for International Chinese Education and International Chinese Online Education. New ways of learning Chinese emerged, such as educational games and intelligent tutoring systems (ITS), some of them based on artificial intelligence. The aim of this systematic review is to examine recent (from 2017 to 2022) research published in ScienceDirect and Scopus databases on the use and impact of educational games and ITS in Chinese language learning. A total of 29 selected studies were analysed. Based on the overall findings, games and ITS are effective tools for Chinese learning, that impact students’ motivation, self-efficacy progress, and learning satisfaction. However, more in-depth research should explore how games and ITS can best be implemented to teach Chinese to foreigners.

Deskilling of Teaching and the Case for Intelligent Tutoring Systems

This essay describes trends in the organization of work that have laid the groundwork for the adoption of interactive AI-driven instruction tools, and the technological innovations that will make intelligent tutoring systems truly competitive with human teachers. Since the origin of occupational specialization, the collection and transmission of knowledge have been tied to individual careers and job roles, specifically doctors, teachers, clergy, and lawyers, the paradigmatic knowledge professionals. But these roles have also been tied to texts and organizations that can disseminate knowledge independently from professionals. Professionals and organizations turn knowledge into texts and tools that enable lay people to access knowledge without the intermediation of professionals or organizations. In the 21st century, one emerging tool for transmitting knowledge is the intelligent tutoring system. This paper examines how technological, epistemic, and economic trends in education are supporting the routinization, proletarianization, and automation of the occupation of teaching, leading to the increasing substitution of intelligent tutoring systems for human instruction.
 Some trends, such as standardized curricula and testing, both restrict teachers’ professional autonomy and facilitate the creation of pedagogical tools. Other trends reduce teachers’ ability to resist automation. The growth of adjunct teaching and paraprofessional roles in higher education allows organizations to take over and rationalize parts of the traditional teacher role. Faculty evaluations and learning outcomes assessment weaken professional claims to be the sole arbiters of instructional quality and student learning. The widespread use of intelligent tutoring systems also depends on the sophistication of software capable of performing the social-emotional and cognitive roles that educators perform. Eventually, pedagogical software will be able to interactively individualize curricula to the needs and interests of every learner, more cheaply, quickly, and accurately than any human teacher. Assessment of learning will be continuous, and certification of learning will be for specific skills instead of broad area competencies. Intelligent tutoring systems will help transition education from its medieval and industrial-era model to more accessible and flexible continuing education for employment and life enrichment.

The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis

Based on systematic research of studies published since the year 2000, this comprehensive meta-analysis investigated how the use of technology can enhance learning in secondary school mathematics and science (grade levels 5–13). All studies (k = 92) compared learning outcomes of students using digital tools to those of a control group taught without the use of digital tools. Overall, digital tool use had a positive effect on student learning outcomes (g = 0.65, p < .001). The provision of teacher trainings on digital tool use significantly moderated the overall effect. Use of intelligent tutoring systems or simulations such as dynamic mathematical tools was significantly more beneficial than hypermedia systems. On a descriptive level, the effect size was larger when digital tools were used in addition to other instruction methods and not as a substitute. The results open up new directions for future research and can inform evidence-based decision-making on the use of digital tools in education.

Intelligent Language Tutoring System

Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language learning and teaching. First, the authors look at ICALL history: its evolution from CALL. Second, issues in ICALL research and integration will be discussed. Third, they will explain how artificial intelligence (AI) techniques are being implemented in language education as ITS and intelligent language tutoring systems (ITLS). Finally, the successful integration and development of ITLS will be explained in detail.

A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems

Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we describe a generalizable approach for more efficient yet rich sensemaking of temporal data during student use of intelligent tutoring systems. This multi-step approach involves using coarse-grain temporality — learning trajectories across knowledge components — to identify and further explore “focal” moments worthy of more fine-grain, context-rich analysis. We discuss the application of this approach to data collected from a classroom study in which students engaged in a Chemistry Virtual Lab tutoring system. We show that the application of this multi-step approach efficiently led to interpretable and actionable insights while making use of the richness of the available data. This method is generalizable to many types of datasets and can help handle large volumes of rich data at multiple levels of granularity. We argue that it can be a valuable approach to tackling some of the most prohibitive methodological challenges involved in temporal learning analytics

Approximately Optimal Teaching of Approximately Optimal Learners

We propose a method of generating teaching policies for use in intelligent tutoring systems (ITS) for concept learning tasks [1] , e.g., teaching students the meanings of words by showing images that exemplify their meanings a la Rosetta Stone [2] and Duo Lingo [3] . The approach is grounded in control theory and capitalizes on recent work by [4] , [5] that frames the “teaching” problem as that of finding approximately optimal teaching policies for approximately optimal learners (AOTAOL). Our work expands on [4] , [5] in several ways: (1) We develop a novel student model in which the teacher's actions can partially eliminate hypotheses about the curriculum. (2) With our student model, inference can be conducted analytically rather than numerically, thus allowing computationally efficient planning to optimize learning. (3) We develop a reinforcement learning-based hierarchical control technique that allows the teaching policy to search through deeper learning trajectories. We demonstrate our approach in a novel ITS for foreign language learning similar to Rosetta Stone and show that the automatically generated AOTAOL teaching policy performs favorably compared to two hand-crafted teaching policies.

Intelligent tutoring systems and learning outcomes: A meta-analysis.

Intelligent Tutoring Systems (ITS) are computer programs that model learners’ psychological states to provide individualized instruction. They have been developed for diverse subject areas (e.g., algebra, medicine, law, reading) to help learners acquire domain-specific, cognitive and metacognitive knowledge. A meta-analysis was conducted on research that compared the outcomes from students learning from ITS to those learning from non-ITS learning environments. The meta-analysis examined how effect sizes varied with type of ITS, type of comparison treatment received by learners, type of learning outcome, whether knowledge to be learned was procedural or declarative, and other factors. After a search of major bibliographic databases, 107 effect sizes involving 14,321 participants were extracted and analyzed. The use of ITS was associated with greater achievement in comparison with teacher-led, large-group instruction (g .42), non-ITS computer-based instruction (g .57), and textbooks or workbooks (g .35). There was no significant difference between learning from ITS and learning from individualized human tutoring (g –.11) or small-group instruction (g .05). Significant, positive mean effect sizes were found regardless of whether the ITS was used as the principal means of instruction, a supplement to teacher-led instruction, an integral component of teacher-led instruction, or an aid to homework. Significant, positive effect sizes were found at all levels of education, in almost all subject domains evaluated, and whether or not the ITS provided feedback or modeled student misconceptions. The claim that ITS are relatively effective tools for learning is consistent with our analysis of potential publication bias.

Serious games: supporting occupational engagement of people based on intelligent tutoring systems aged 50+

This paper offers an overview of the requirements of serious games to support occupational engagement for people at age 50 and above, and the features for such a serious game. A comprehensive literature revision and a questionnaire to 23 active in the field health professionals of the Valparaiso region (Chile) were performed. In the end, among the various requirements stated, primary requirements were the evaluation of adaptation skills and organizing space and objects skills as well as of information exchange skills and coordination skills, with respect to occupation-based interventions and routines within a social context. Afterwards, some primary features were stated, such as the use of intelligent tutoring systems, adaptability, difficulty balance, real-time strategy, non linear stories, and spatial, temporal and adversaries reasoning. In the discussion about the features, issues of purpose, strategy and the personalization of a serious game for people aged 50 and older were specified in detail

Actions Towards the Application of Intelligent Systems in Computer Education

In this work we present two courses of action as part of a research project which is focused in the use of intelligent tutoring systems and robots to improve the learning process of programming for the engineering field. The first method we use is the application of neural networks to the prediction of academic performance of students of programming and, on the other hand, we are developing practical activities based in robot programming for the same student universe as a motivational tool. These actions are the first steps to a learning strategy based on feedback and motivation, by applying intelligent systems in the process of teaching and learning programming applied to engineering field.

The Design of an ITS-Based Business Simulation: A New Epistemology for Learning

This article discusses the design and use of an intelligent tutoring system (ITS)for computerized business simulations. It is argued that intelligent tutoring systems are a natural complement to computerized business simulations and could significantly enhance their pedagogical effectiveness. The use of ITS as an instructional technology and its success are reviewed. Afundamental modelforan ITS-based business simulation ispresented. The user interface and the linkbetween the ITS and the simulation are examined. It is recommended that the ITS consist of three distinct modules: expert consultant, diagnostic testing, andpedagogical support. The integration of ITS with business simulations offers a new epistemologyfor learning, the significance of which has yet to be fully understood.

Development of a Real-Time Simulation with Intelligent Tutoring Capabilities

Training programs for complex tasks are increasingly using simulations to provide transfer of training to the job environment without incurring high costs of on-the-job training. A second trend in training is toward the use of intelligent tutoring systems (ITSs) to provide individualized feedback to optimize training. Combining simulation with an ITS can be especially beneficial, but use of intelligent tutoring mechanisms such as expert systems is often difficult in a complex, realtime environment. In this paper, we describe the development of a proof-of-concept training program that combines F-16 flight simulation with an embedded real-time intelligent tutoring system. In the simulation, pilots learn the correct use of advanced fire control radar modes to locate and assess multiple enemy formations (search and sort tasks). The expert system monitors pilot behavior and verbal responses as the pilot flies the simulation. At certain critical points, if the pilot's performance has fallen outside of pre-specified parameters of “safe” behavior, the tutoring component stops the simulation and feedback is provided.