Use of an intelligent tutoring system for a curriculum on augmentative and alternative communication: Feasibility for implementation.

BACKGROUND: Over 8 million children with disabilities live in Africa and are candidates for augmentative and alternative communication (AAC), yet formal training for team members, such as speech-language therapists and special education teachers, is extremely limited. Only one university on the continent provides postgraduate degrees in AAC, and other institutions provide only short modules at an undergraduate level. The need for an introductory training course on AAC that is accessible by university students continent-wide was identified. An online programme, namely an intelligent tutoring system (ITS), was identified as a possible option to facilitate interactive learning without the need for synchronous teaching. The use of an ITS is shown to be effective in developing knowledge and clinical reasoning in the health and rehabilitation fields. However, it has not yet been applied to student teaching in the field of AAC. AIM: To determine both the feasibility of an ITS to implement an AAC curriculum for students in four African countries, and the usability and effectiveness of such a system as a mechanism for learning about AAC. METHOD & PROCEDURES: The study included two components: the development of a valid AAC curriculum; and using the ITS to test the effectiveness of implementation in a pre- and post-test design with 98 speech-language therapy and special education students from five universities. OUTCOMES & RESULTS: Statistically significant differences were obtained between pre- and post-test assessments. Students perceived the learning experience as practical, with rich content. CONCLUSIONS & IMPLICATIONS: The findings suggest that the ITS-based AAC curriculum was positively perceived by the students and potentially offers an effective means of providing supplementary AAC training to students, although modifications to the system are still required. WHAT THIS PAPER ADDS: What is already known on the subject Professionals typically lack formal training in AAC. In Africa, this presents a serious challenge as there are over 8 million children who are candidates for AAC. A need for an introductory training course on AAC, which can be accessed by university students continent-wide, was identified. What this paper adds to existing knowledge An AAC curriculum was developed and integrated into an ITS, an online programme allowing interactive learning through asynchronous teaching. Students from four African countries completed the AAC ITS curriculum. The curriculum was positively received by the students and statistically significant changes in knowledge were identified. What are the practical and clinical implications of this work? This feasibility study shows that the use of an ITS is an effective means of providing AAC training to university students in these African countries. The results provide a valuable contribution toward ensuring the equitable distribution of AAC training opportunities in the African context. This will have a significant positive impact on those who are candidates for AAC.

Introducing AnatomyGPT: A customized artificial intelligence application for anatomical sciences education.

Artificial intelligence (AI) technologies are poised to become an increasingly important part of education in the anatomical sciences. OpenAI has also introduced generative pretrained transformers (GPTs), which are customizable versions of the standard ChatGPT application. There is little research that has explored the potential of GPTs to serve as intelligent tutoring systems for learning the anatomical sciences. The objective of this study was to describe the design and explore the performance of AnatomyGPT, a customized artificial intelligence application intended for anatomical sciences education. The AnatomyGPT application was configured with GPT Builder by uploading open-source textbooks as knowledge sources and by providing pedagogical instructions for how to interact with users. The performance of AnatomyGPT was compared with ChatGPT by evaluating the responses of both applications to prompts of the National Board of Medical Examiners (NBME) sample items with respect to accuracy, rationales, and citations. AnatomyGPT achieved high scores on the NBME sample items for Gross Anatomy, Embryology, Histology, and Neuroscience and scored comparably to ChatGPT. In addition, AnatomyGPT provided several citations in the responses that it generated, while ChatGPT provided none. Both GPTs provided rationales for all sample items. The customized AnatomyGPT application demonstrated preliminary potential as an intelligent tutoring system by generating responses with increased citations as compared with the standard ChatGPT application. The findings of this study suggest that instructors and students may wish to create their own custom GPTs for teaching and learning anatomy. Future research is needed to further develop and characterize the potential of GPTs for anatomy education.

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.

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.

Mathematics intelligent tutoring system for learning multiplication and division of fractions based on diagnostic teaching.

A one-on-one dialogue-based mathematics intelligent tutoring system (ITS) for learning multiplication and division of fractions was developed and evaluated in this study. This system could identify students' error types and misconceptions in real-time by using a block-based matching method. The adaptive dialogue-based instruction was supported by a response-driven tutoring model, which was constructed based on the diagnostic teaching methodology. Instructional strategies including provoking cognitive conflict, problem simplification and representational teaching were used in the tutoring model of the system. Effectiveness of the math ITS in remedial instruction was evaluated through a quasi-experimental study. The participants of the study were 66 sixth graders chosen from central Taiwan. They were divided into an experimental group of 35 and a control group of 31. One week after the pretest, the experimental group received 2-h one-on-one instruction via the math ITS, while the control group took a 2-h conventional teacher instruction with the same teaching content in the classroom. All participants took a post-test within 2 days after the remedial instruction. The results showed that the experimental group using the math ITS significantly outperformed the control group. Further analysis indicated that the math ITS had a significant effect on the lesser-performing group (the lower 75% in the pretest score). In addition, a usability and user experience survey showed that students were willing and likely to learn mathematics using the dialogue-based math ITS.

Designing an intelligent tutoring system for computer programing in the Pacific.

Intelligent Tutoring Systems (ITSs) are educational systems that reflect knowledge using artificial intelligence implements. In this paper, we give an outline of the Programming-Tutor architectural design with the core implements on user interaction. This pilot proposal is for designing a model domain of a subset in the computer programming language. The completed project would be adequate to show the idea of a completely developed computing Intelligent Tutoring System in online programming courses to offer benefits to students in the Pacific. This proposed concept would also provide students with an immersive learning experience in an online course to assist in a formative assessment to enhance student learning. A smart tutoring system can provide prompt input of high quality which not only conveys to students about the consistency of the solution but also provides them with information on the precision of the key concerning their existing solutions expertise. This Intelligent Tutoring System (ITS) is proposed to be designed using intelligent algorithms such as optimized ant colony to be able to support the online tutoring system that can initiate the complex learning principles in computing science courses. It is also hypothesized that, based on the performance of other Intelligent Tutoring Systems, students would be able to learn to program more easily in regional campuses and acquire experiences more rapidly and efficiently than students who are taught using conventional methods in an online mode.

Self-Guided Web-Based Interventions: Scoping Review on User Needs and the Potential of Embodied Conversational Agents to Address Them.

BACKGROUND: Web-based mental health interventions have evolved from innovative prototypes to evidence-based and clinically applied solutions for mental diseases such as depression and anxiety. Open-access, self-guided types of these solutions hold the promise of reaching and treating a large population at a reasonable cost. However, a considerable factor that currently hinders the effectiveness of these self-guided Web-based interventions is the high level of nonadherence. The absence of a human caregiver apparently has a negative effect on user adherence. It is unknown to what extent this human support can be handed over to the technology of the intervention to mitigate this negative effect. OBJECTIVE: The first objective of this paper was to explore what is known in literature about what support a user needs to stay motivated and engaged in an electronic health (eHealth) intervention that requires repeated use. The second objective was to explore the current potential of embodied conversational agents (ECAs) to provide this support. METHODS: This study reviews and interprets the available literature on (1) support within eHealth interventions that require repeated use and (2) the potential of ECAs by means of a scoping review. The rationale for choosing a scoping review is that the subject is broad, diverse, and largely unexplored. Themes for (1) and (2) were proposed based on grounded theory and mapped on each other to find relationships. RESULTS: The results of the first part of this study suggest the presence of user needs that largely remain implicit and unaddressed. These support needs can be categorized as task-related support and emotion-related support. The results of the second part of this study suggest that ECAs are capable of engaging and motivating users of information technology applications in the domains of learning and behavioral change. Longitudinal studies must be conducted to determine under what circumstances ECAs can create and maintain a productive user relationship. Mapping the user needs on the ECAs' capabilities suggests that different kinds of ECAs may provide different solutions for improving the adherence levels. CONCLUSIONS: Autonomous ECAs that do not respond to a user's expressed emotion in real time but take on empathic roles may be sufficient to motivate users to some extent. It is unclear whether those types of ECAs are competent enough and create sufficient believability among users to address the user's deeper needs for support and empathy. Responsive ECAs may offer a better solution. However, at present, most of these ECAs have difficulties to assess a user's emotional state in real time during an open dialogue. By conducting future research with relationship theory-based ECAs, the added value of ECAs toward user needs can be better understood.

Active engagement in a web-based tutorial to prevent obesity grounded in Fuzzy-Trace Theory predicts higher knowledge and gist comprehension.

We used Sharable Knowledge Objects (SKOs) to create an Intelligent Tutoring System (ITS) grounded in Fuzzy-Trace Theory to teach women about obesity prevention: GistFit, getting the gist of healthy eating and exercise. The theory predicts that reliance on gist mental representations (as opposed to verbatim) is more effective in reducing health risks and improving decision making. Technical information was translated into decision-relevant gist representations and gist principles (i.e., healthy values). The SKO was hypothesized to facilitate extracting these gist representations and principles by engaging women in dialogue, "understanding" their responses, and replying appropriately to prompt additional engagement. Participants were randomly assigned to either the obesity prevention tutorial (GistFit) or a control tutorial containing different content using the same technology. Participants were administered assessments of knowledge about nutrition and exercise, gist comprehension, gist principles, behavioral intentions and self-reported behavior. An analysis of engagement in tutorial dialogues and responses to multiple-choice questions to check understanding throughout the tutorial revealed significant correlations between these conversations and scores on subsequent knowledge tests and gist comprehension. Knowledge and comprehension measures correlated with healthier behavior and greater intentions to perform healthy behavior. Differences between GistFit and control tutorials were greater for participants who engaged more fully. Thus, results are consistent with the hypothesis that active engagement with a new gist-based ITS, rather than a passive memorization of verbatim details, was associated with an array of known psychosocial mediators of preventive health decisions, such as knowledge acquisition, and gist comprehension.

Understanding Genetic Breast Cancer Risk: Processing Loci of the BRCA Gist Intelligent Tutoring System.

The BRCA Gist Intelligent Tutoring System helps women understand and make decisions about genetic testing for breast cancer risk. BRCA Gist is guided by Fuzzy-Trace Theory, (FTT) and built using AutoTutor Lite. It responds differently to participants depending on what they say. Seven tutorial dialogues requiring explanation and argumentation are guided by three FTT concepts: forming gist explanations in one's own words, emphasizing decision-relevant information, and deliberating the consequences of decision alternatives. Participants were randomly assigned to BRCA Gist, a control, or impoverished BRCA Gist conditions removing gist explanation dialogues, argumentation dialogues, or FTT images. All BRCA Gist conditions performed significantly better than controls on knowledge, comprehension, and risk assessment. Significant differences in knowledge, comprehension, and fine-grained dialogue analyses demonstrate the efficacy of gist explanation dialogues. FTT images significantly increased knowledge. Providing more elements in arguments against testing correlated with increased knowledge and comprehension.

Integrating FHIR and UMLS in an Intelligent Tutoring System.

Our novel Intelligent Tutoring System (ITS) architecture integrates HL7 Fast Healthcare Interoperability Resources (FHIR) for data exchange and Unified Medical Language System (UMLS) codes for content mapping.

InterAgent on FHIR - Lessons Learned from Implementing an Intelligent Tutoring System with the Help of HL7 FHIR.

INTRODUCTION: For an interoperable Intelligent Tutoring System (ITS), we used resources from Fast Healthcare Interoperability Resources (FHIR) and mapped learning content with Unified Medical Language System (UMLS) codes to enhance healthcare education. This study addresses the need to enhance the interoperability and effectiveness of ITS in healthcare education. STATE OF THE ART: The current state of the art in ITS involves advanced personalized learning and adaptability techniques, integrating technologies such as machine learning to personalize the learning experience and to create systems that dynamically respond to individual learner needs. However, existing ITS architectures face challenges related to interoperability and integration with healthcare systems. CONCEPT: Our system maps learning content with UMLS codes, each scored for similarity, ensuring consistency and extensibility. FHIR is used to standardize the exchange of medical information and learning content. IMPLEMENTATION: Implemented as a microservice architecture, the system uses a recommender to request FHIR resources, provide questions, and measure learner progress. LESSONS LEARNED: Using international standards, our ITS ensures reproducibility and extensibility, enhancing interoperability and integration with existing platforms.

Only a matter of time? Using logfile data to evaluate temporal motivation theory in university students' examination preparation.

BACKGROUND: While previous research has emphasized the importance of personal beliefs (expectancy-value theories) for achievement-motivated behaviour, it lacks the integration of temporal factors that are also discussed as important drivers of achievement-motivated behaviour. Temporal Motivation Theory (TMT) combines both approaches in a formalized manner. AIMS: Although TMT is supported by empirical studies with self-reported academic procrastination, it has not been tested on actual achievement-motivated behaviour. MATERIALS & METHODS: We evaluated the predictive power of the TMT on N = 2351 learning days of 127 psychology students' self-regulated examination preparation for statistics over the course of one semester using logfile data of an e-learning system. RESULTS: The proposed TMT score, incorporating expectancy and value beliefs, sensivitiy to delay, and actual time till examination predicted students' achievement-motivated behaviour significantly. DISCUSSION: Further analyses revealed that not the trait compositions of the TMT, but the temporal proximity of the statistics examination was the main driver of this association. CONCLUSION: The results have important implications for understanding the factors that shape students' motivation to learn and subsequent academic success in actual learning situations. Thus, research should continue to take situational aspects, especially the temporal proximity of goals more into account.

Performance increases in mathematics during COVID-19 pandemic distance learning in Austria: Evidence from an intelligent tutoring system for mathematics.

BACKGROUND: In 2020, school closures during the COVID-19 pandemic forced students all over the world to promptly alter their learning routines from in-person to distance learning. However, so far, only a limited number of studies from a few countries investigated whether school closures affected students' performance within intelligent tutoring system-such as intelligent tutoring systems. METHOD: In this study, we investigated the effect of school closures in Austria by evaluating data (n = 168 students) derived from an intelligent tutoring system for learning mathematics, which students used before and during the first period of school closures. RESULTS: We found that students' performance increased in mathematics in the intelligent tutoring system during the period of school closures compared to the same period in previous years. CONCLUSION: Our results indicate that intelligent tutoring systems were a valuable tool for continuing education and maintaining student learning during school closures in Austria.

Intelligent tutoring systems for word problem solving in COVID-19 days: could they have been (part of) the solution?

The COVID-19 pandemic led to the lockdown of schools in many countries, forcing teachers and students to carry out educational activities remotely. In the case of mathematics, developing remote instruction based on both synchronous and asynchronous technological solutions has proven to be an extremely complex challenge. Specifically, this was the case in topics such as word problem solving, as this domain requires intensive supervision and feedback from the teacher. In this piece of research, we present an evaluation of how technology is employed in the teaching of mathematics, with particular relevance to learning during the pandemic. For that purpose, we conducted a systematic review, revealing the almost complete absence of experiments in which the use of technology is not mediated by the teacher. These results reflect a pessimistic vision within the field of mathematics education about the possibilities of learning when the student uses technology autonomously. Bringing good outcomes out of a bad situation, the pandemic crisis may represent a turning point from which to start directing the research gaze towards technological environments such as those mediated by artificial intelligence. As an example, we provide a study illustrating to what extent intelligent tutoring systems can be cost-effective compared to one-to-one human tutoring and mathematic learning-oriented solutions for intensive supervision in the teaching of word problem solving, especially appropriate for remote settings. Despite the potential of these technologies, the experience also showed that student socioeconomic level was a determining factor in the participation rate with an intelligent tutoring system, regardless of whether or not the administration guaranteed students' access to technological resources during the COVID-19 situation.

State-Aware Deep Item Response Theory using student facial features.

This paper introduces a novel approach to Item Response Theory (IRT) by incorporating deep learning to analyze student facial expressions to enhance the prediction and understanding of student responses to test items. This research is based on the assertion that students' facial expressions offer crucial insights into their cognitive and affective states during testing, subsequently influencing their item responses. The proposed State-Aware Deep Item Response Theory (SAD-IRT) model introduces a new parameter, the student state parameter, which can be viewed as a relative subjective difficulty parameter. It is latent-regressed from students' facial features while solving test items using state-of-the-art deep learning techniques. In an experiment with 20 students, SAD-IRT boosted prediction performance in students' responses compared to prior models without the student state parameter, including standard IRT and its deep neural network implementation, while maintaining consistent predictions of student ability and item difficulty parameters. The research further illustrates the model's early prediction ability in predicting the student's response result before the student answered. This study holds substantial implications for educational assessment, laying the groundwork for more personalized and effective learning and assessment strategies that consider students' emotional and cognitive states.

The potential of learning with (and not from) artificial intelligence in education.

AI-powered technologies are increasingly being developed for educational purposes to contribute to students' academic performance and overall better learning outcomes. This exploratory review uses the PRISMA approach to describe how the effectiveness of AI-driven technologies is being measured, as well as the roles attributed to teachers, and the theoretical and practical contributions derived from the interventions. Findings from 48 articles highlighted that learning outcomes were more aligned with the optimization of AI systems, mostly nested in a computer science perspective, and did not consider teachers in an active role in the research. Most studies proved to be atheoretical and practical contributions were limited to enhancing the design of the AI system. We discuss the importance of developing complementary research designs for AI-powered tools to be integrated optimally into education.

RALF: an adaptive reinforcement learning framework for teaching dyslexic students.

Dyslexia is a learning disorder in which individuals have significant reading difficulties. Previous studies found that using machine learning techniques in content supplements is vital in adapting the course concepts to the learners' educational level. However, to the best of our knowledge, no research objectively applied machine learning methods to adaptive content generation. This study introduces an adaptive reinforcement learning framework known as RALF through Cellular Learning Automata (CLA) to generate content automatically for students with dyslexia. At first, RALF generates online alphabet models as a simplified font. CLA structure learns each rule of character generation through the reinforcement learning cycle asynchronously. Second, Persian words are generated algorithmically. This process also considers each character's state to decide the alphabet cursiveness and the cells' response to the environment. Finally, RALF can generate long texts and sentences using the embedded word-formation algorithm. The spaces between words are proceeds through the CLA neighboring states. Besides, RALF provides word pronunciation and several exams and games to improve the learning performance of people with dyslexia. The proposed reinforcement learning tool enhances students' learning rate with dyslexia by almost 27% compared to the face-to-face approach. The findings of this research show the applicability of this approach in dyslexia treatment during Lockdown of COVID-19.

The double-edged sword effect of argumentative scaffolding on group discussion in an adaptive discussion system.

Group discussion is a common and important form of learning. The effectiveness of group discussion could be facilitated by the adaptive support of virtual agent. Argumentative knowledge construction is beneficial to learners' acquisition of knowledge, but the effectiveness of argumentative scaffolding is not consistent in existing studies. In this study, a total of 64 college students (32 groups, two participants and one computer agent in each group) participated in the experiment and they were assigned to the experimental condition (16 groups) and the control condition (16 groups). In the control condition, the computer agent would give an idea from semantically different categories according to the automatic categorization of the current discussion. In the experimental condition, the computer agent provided argumentative scaffolding after giving diverse ideas to support participants' deep processing. The argumentative scaffolding included two prompt questions, "do you agree with me?" and "could you give the reasons to support your viewpoint?." The dependent variables were the interaction quality, network centrality, the breadth and depth of discussion, the self-reported of discussion effectiveness and the degree of change before and after the discussion. Findings revealed that compared with the control condition, the participants were more likely to discuss the keywords provided by the virtual agent and reported more comprehensive understanding of the discussion topic, but surveyed less ideas and interactions during the discussion under the argumentative condition. This study suggests that the argumentative scaffolding may have both positive and negative effect on the group discussion and it's necessary to make a choice.

A comparative analysis of metaheuristics applied to adaptive curriculum sequencing.

The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content-called Adaptive Curriculum Sequencing (ACS)-is important to avoid cognitive overload and disorientation. The search for an optimal sequence from ever-growing databases is an NP-Hard combinatorial optimization problem. Although some approaches have been proposed, it is challenging to assess their contributions due to the lack of benchmark data available. This paper presents a procedure to create synthetic dataset to evaluate ACS approaches and, as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO) and Prey-Predator Algorithm using student's learning goals and their extrinsic and intrinsic information. We also propose an approach based on Differential Evolution (DE). The computational experiments include synthetic datasets with a varied amount of learning materials and real-world datasets for comparison. The results show that DE performed better than the other methods when less than 500 learning materials are used while PSO performed better for larger problems.

Simulating the dynamics of self-regulation, emotion, grit, and student performance in cyber-learning environments.

This research presents the results of development and validation of the Cyclical Self-Regulated Learning (SRL) Simulation Model, a model of student cognitive and metacognitive experiences learning mathematics within an intelligent tutoring system (ITS). Patterned after Zimmerman and Moylan's (2009) Cyclical SRL Model, the Simulation Model depicts a feedback cycle connecting forethought, performance and self-reflection, with emotion hypothesized as a key determinant of student learning. A mathematical model was developed in steps, using data collected from students during their sessions within the ITS, developing solutions using structural equation modeling, and using these coefficients to calibrate a System Dynamics (SD) Simulation model. Results provide validation of the Cyclical SRL Model, confirming the interplay of grit, emotion, and performance in the ITS. The Simulation Model enables mathematical simulations depicting a variety of student background types and intervention styles and supporting deeper future explorations of dimensions of student learning.