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Educating Artificial Intelligence following the Child Learning Development Trajectories2024Artificial Intelligence is spreading in most daily activities. However, its develop- ment and deployment raise issues related to biases, such as gender and disability, mainly stemming from biased or incomplete datasets and lack of transparency and accountability in its algorithms. To overcome these issues, it is necessary to revert to a human-centered mindset, trying to educate algorithms rather than only train them. Adopting a human-centered approach in AI has been a first step, but it is necessary a step ahead. Indeed, recent theoretical perspectives suggest that edu- cating AI algorithms also need a profound understanding of the context of use in which it operates, adopting an approach like those in which a child is educated from birth following its developmental trajectory. By incorporating well-established educational models into the training of AI algorithms, intelligent systems based on those AI algorithms can better align with human learning trajectories, reducing bi- ases and making them more contextually aware. This paper goes in this direction, presenting an educational human-centered approach as a design methodology for artificial intelligence algorithms used within the European FRACTAL project. This proposal would pave the way toward developing more educated artificial intelligence algorithms since they are adapted to the real context of use.

UX of AI & PersonalizationResearch Papers

Visualizing the knowledge mapping of artificial intelligence in education: A systematic reviewInformation Technologies · 2024Artificial Intelligence (AI) plays a vital role in the growth and progress of educa- tion. Therefore, there is a need to scientifically explore the application of Artifi- cial Intelligence in Education (AIED) and systematically analyze the development trends and research hotspots of AIED to provide reference for researchers. In this study, 1356 articles (2016–2023) in WOS were selected for further research, utiliz- ing knowledge graph analysis. Using both VOSviewer and CiteSpace, which facili- tated triangulating the data across software platforms to ensure the reliability of the results, the main highly co-cited literature and keywords were thoroughly analyzed. The key highlights of the results are: Firstly, the study reveals three major themes in the field of AI education, namely, medical theme, educational theme, and ChatGPT theme. Secondly, important literature and nodes in the field of AIED were identi- fied. Thirdly, the study demonstrates the main technologies in the field of AIED, including Natural Language Processing, Machine Learning, Deep Learning, and Generative Artificial Intelligence. Finally, the burst analysis illustrates the hotspots and themes of the AIED at different stages. This study enriches the understanding of the fundamental knowledge and research frontiers essential to the application of AIED, which helps identify the patterns and trends for future research and teaching practices.

UX of AI & PersonalizationResearch Papers

Unveiling the landscape of generative artificial intelligence in education: a comprehensive taxonomy of applications, challenges, and future prospectsInformation Technologies · 2024The rapid advancement of Generative Artificial Intelligence (GenAI) models, particularly ChatGPT, has sparked widespread discussion among educators and researchers regarding their potential implications for education. This study presents a comprehensive taxonomy of GenAI in academia and education, encompassing a wide range of applications, challenges, ethical considerations, and future prospects. Drawing on a scoping review of 453 articles, including the 50 most cited works throughout 2023, the taxonomy provides a state-of-the-art analysis of the current landscape of GenAI in education. The taxonomy offers a theoretical framework that aligns with the current discourse in GenAI and education, providing a critical evaluation of the existing literature and proposing innovative perspectives and solu- tions. The practical implications of the taxonomy for educators, researchers, and policymakers are highlighted, emphasizing the need for ethical considerations and informed policies to maximize the benefits of GenAI while minimizing its risks and negative impacts.

UX of AI & PersonalizationResearch Papers

Does personality matter: examining the value of personality insights for personalized nudges that encourage the selection of learning resourcesCardenas Canto, dimitrova · 2024© 2024 Cardenas Canto, Dimitrova, Sherman and Flint. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

UX of AI & PersonalizationResearch Papers

Large Language Models Meet User Interfaces: The Case of Provisioning FeedbackStanislav Pozdniakov, Jonathan Brazil et al. · 2024- a *School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, QLD, 4072, Australia* - b *Institute for Teaching and Learning Innovation, The University of Queensland, St Lucia, QLD, 4072, Australia* - c *Centre for Learning Analytics, Faculty of Information Technology, Monash University, Melbourne, VIC, 3800, Australia* - d *School of Computer Science, University of Auckland, 38 Princes Street Auckland, 1010, New Zealand*

UX of AI & PersonalizationResearch Papers

Roles of artificial intelligence experience, information redundancy, and familiarity in shaping active learning: Insights from intelligent personal assistantsWang, Sun · 2023Artificial Intelligence (AI) is increasingly being integrated into educational settings, with Intelligent Personal Assistants (IPAs) playing a significant role. However, the psychological impact of these AI assistants on fostering active learning behaviors needs to be better understood. This research study addresses this gap by proposing a theoretical model to outline and predict active learning dynamics. Data was col- lected from 237 validated questionnaires and analyzed using partial least squares structural equation modeling. Our results confirm most hypotheses advanced in our model, and information redundancy has an unexpected negative and indirect influ- ence on active learning, while perceived familiarity and system quality are positive drivers. Crucial mediators such as perceived usefulness, ease of use, and conveni- ence significantly positively influence active learning outcomes. Interestingly, the relationship between perceived ease of use, perceived convenience, and active learn- ing is positively moderated by AI experience. The most striking and unexpected finding of this study is the preference of university students for familiar systems over high-tech learning methods. This result challenges the common belief that the younger generation is always eager to adopt the latest technology. Instead, our find- ings suggest that students value convenience and familiarity over novelty in learn- ing systems. This preference is reflected in their systematic evaluation, where con- venience and familiarity are considered top priorities. This study provides valuable insights into the potential of AI to enrich the learning experience, thus making it especially relevant to professionals interested in artificial intelligence in interna- tional business education.

UX of AI & PersonalizationResearch Papers

Choice-based Personalization in MOOCs: Impact on Activity and Perceived ValueIlana Ram · 2023Personalization in education describes instruction that is tailored to learners’ inter­ ests, attributes, or background and can be applied in various ways, one of which is through choice. In choice-based personalization, learners choose topics or resources that fit them the most. Personalization may be especially important (and under- used) with diverse learners, such as in a MOOC context. We report the impact of choice-based personalization on activity level, learning gains, and satisfaction in a Climate Science MOOC. The MOOC’s learning assignments had learners choose resources on climate-related issues in either their geographic locale (Personalized group) or in given regions (Generic group). 219 learners completed at least one of the two assignments. Over the entire course, personalization increased learn­ ers’ activity (number of course events), self-reported understanding of local issues, and self-reported likelihood to change climate-related habits. We found no differ­ ences on assignment completion rate, assignment length, and self-reported time-on- task. These results show that benefits of personalization extend beyond the original task and affect learners’ overall experience. We discuss design and implications of choice-based personalization, as well as opportunities for choice-based personaliza­ tion at scale.

UX of AI & PersonalizationResearch Papers

Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential EducationYanmin Li · 2022Li Y, Zhong Z, Zhang F and Zhao X (2022) Artificial Intelligence-Based Human–Computer Interaction Technology Applied in Consumer Behavior Analysis and Experiential Education. Front. Psychol. 13:784311. doi: 10.3389/fpsyg.2022.784311 In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human–computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.

UX of AI & PersonalizationResearch Papers

A review of AI teaching and learning from 2000 to 2020Davy Tsz Kit Ng, Min Lee et al. · 2022In recent years, with the popularity of AI technologies in our everyday life, research- ers have begun to discuss an emerging term “AI literacy”. However, there is a lack of review to understand how AI teaching and learning (AITL) research looks like over the past two decades to provide the research basis for AI literacy education. To summarize the empirical findings from the literature, this systematic literature review conducts a thematic and content analysis of 49 publications from 2000 to 2020 to pave the way for recent AI literacy education. The related pedagogical mod- els, teaching tools and challenges identified help set the stage for today’s AI literacy. The results show that AITL focused more on computer science education at the uni- versity level before 2021. Teaching AI had not become popular in K-12 classrooms at that time due to a lack of age-appropriate teaching tools for scaffolding support. However, the pedagogies learnt from the review are valuable for educators to reflect how they should develop students’ AI literacy today. Educators have adopted collab- orative project-based learning approaches, featuring activities like software develop- ment, problem-solving, tinkering with robots, and using game elements. However, most of the activities require programming prerequisites and are not ready to scaf- fold students’ AI understandings. With suitable teaching tools and pedagogical sup- port in recent years, teaching AI shifts from technology-oriented to interdisciplinary design. Moreover, global initiatives have started to include AI literacy in the lat- est educational standards and strategic initiatives. These findings provide a research foundation to inform educators and researchers the growth of AI literacy education that can help them to design pedagogical strategies and curricula that use suitable technologies to better prepare students to become responsible educated citizens for today’s growing AI economy.

UX of AI & PersonalizationResearch Papers

X5Learn: A Personalised Learning Companion at the Intersection of AI and HCIMaría Pérez-Ortiz, Claire Dormann et al. · 2021X5Learn (available at https://x5learn.org) is a human-centered AI- powered platform for supporting access to free online educational resources. X5Learn provides users with a number of educational tools for interacting with open educational videos, and a set of tools adapted to suit the pedagogical preferences of users. It is intended to support both teachers and students, alike. For teachers, it provides a powerful platform to reuse, revise, remix, and redistribute open courseware produced by others. These can be videos, pdfs, exercises and other online material. For students, it provides a scaffolded and informative interface to select content to watch, read, make notes and write reviews, as well as a powerful personalised recommenda- tion system that can optimise learning paths and adjust to the user’s learning preferences. What makes X5Learn stand out from other educational platforms, is how it combines human-centered design with AI algorithms and software tools with the goal of making it intuitive and easy to use, as well as making the AI transparent to the user. We present the core search tool of X5Learn, intended to support exploring open educational materials. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). IUI ’21 Companion, April 14–17, 2021, College Station, TX, USA © 2021 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8018-8/21/04. https://doi.org/10.1145/3397482.3450721 CCS CONCEPTS • Information systems →Users and interactive retrieval; Per- sonalization; Recommender systems; Search interfaces; • Ap- plied computing →Interactive learning environments.

UX of AI & PersonalizationResearch Papers

Rule based adaptive user interface for adaptive E-learning systemManohara Pai M. M. · 2018The term Adaptive E-learning System (AES) refers to the set of techniques and approaches that are combined together to offer online courses to the learners with the aim of providing customized resources and interfaces. Most of these systems focus on adaptive contents which are generated to the learners without considering the learning styles of the learners. Learning style of the learner defines the way of learning the contents. The system should not only meet the individual need of the contents but also the customized user interface on the portal. Hence, an AES should mainly focus on recommending learning contents with Adaptive User Interface (AUI) on the portal. The work in the paper proposes a generic approach to provide the learning contents with AUI components based on the learning styles of the learners. The learning style adopted for the work is the Felder-Silverman Learning Style Model (FSLSM). The proposed approach defines generic rules which are generated automatically for any online course with the adaptive contents. Also, the approach takes care of new learners by providing learning path as a user interface component on the portal. The experiment has been conducted on engineering students for a particular online course. The portal is validated using parameters of usability testing by generating test cases and statistical analysis has been carried out to identify the impact of AUI components on the learning process. The result shows the well adaptation of user interface components and contents based on learning styles.

UX of AI & PersonalizationResearch Papers

Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and FeaturesClaude Frasson, Nathan C. Hall · 2016A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven ap- proaches to enhance learners’ experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic’s breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotion- aware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006, 2011) and its assumptions about the relationship between distal and proximal antecedents and the elicitation and Int J Artif Intell Educ (2017) 27:268–297 DOI 10.1007/s40593-016-0126-8 Note. This manuscript is based on an extended version of: Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N.C. (2015a). An integrated emotion-aware framework for intelligent tutoring systems. In C. Conati & N. Heffernan (Eds.), Lectures Notes in Artificial Intelligence: Vol. 9112. Artificial Intelligence in Education (pp. 620-624). Switzerland: Springer. * Jason M. Harley jharley1@ualberta.ca 1 Educational Psychology, University of Alberta, 6-102 Education North, Edmonton, AB T6G 2G5, Canada 2 Educational and Counselling Psychology, McGill University, 3700 McTavish Street 614, Montréal, QC H3A 1Y2, Canada 3 Computer Science and Operations Research, Université de Montréal, 2920 Chemin de la Tour, Pavillon André-Aisenstadt 2194, Montréal, QC H3C 3J7, Canada regulation of emotion. The taxonomy expands upon a dichotomy of emotion-aware systems proposed by D’Mello and Graesser (2015) and is intended to guide the design of emotion-aware systems that can fos

UX of AI & PersonalizationResearch Papers

e-Learning Experience using Recommender SystemsJesus Bobadill, Antonio Hernando et al. · 2011This paper presents the results obtained using a real e-learning recommender system where the collaborative filtering core has been adapted with the aim of weighting the importance of the recommendations in accordance with the users’ knowledge. In this way, ratings from users with better knowledge of the given subject will have greater importance over ratings from users with less knowledge. In the same way, we validate the results obtained and we adjust, with just one parameter, the weight that should be awarded, in each specific e-learning recommender system, to the ratings of the users with the best reputation. The results obtained show a notable improvement regarding traditional collaborative filtering methods and suggest balanced weightings between the importance assigned to users with more or less knowledge. Categories and Subject Descriptors K.3.2 [Computer and Information Science Education] General Terms Experimentation.

UX of AI & PersonalizationResearch Papers

A review of immersive virtual reality serious games to enhance learning and trainingDavid Checa, Andres Bustillo · 2020The merger of game-based approaches and Virtual Reality (VR) environments that can enhance learning and training methodologies have a very promising future, reinforced by the widespread market-availability of affordable software and hardware tools for VR-environ- ments. Rather than passive observers, users engage in those learning environments as active participants, permitting the development of exploration-based learning paradigms. There are separate reviews of VR technologies and serious games for educational and training purposes with a focus on only one knowledge area. However, this review covers 135 proposals for serious games in immersive VR-environments that are combinations of both VR and serious games and that offer end-user validation. First, an analysis of the forum, nationality, and date of publication of the articles is conducted. Then, the application domains, the target audience, the design of the game and its technological implementation, the performance evaluation proce- dure, and the results are analyzed. The aim here is to identify the factual standards of the proposed solutions and the differences between training and learning applications. Finally, the study lays the basis for future research lines that will develop serious games in immersive VR- environments, providing recommendations for the improvement of these tools and their successful application for the enhancement of both learning and training tasks.

Emotional DesignResearch Papers

Can emotional design really evoke emotion in multimedia learning?2020The aim of this study was to investigate the effects of emotional design on learners’ performance and emotions. If emotional design has any effects on learners’ performance and emotions, then we attempted to explore dimensions that are accounted for such effects. 316 Chinese university students participated in this study. The students were divided into two groups: NED group - students in this group learned material based on neutral design, and PED group - students in this group learned material based on positive design. We explored the differences between two groups in student learning performance as well as emotion. Prior knowledge, retention, and transfer tests were also carried out and outcomes were compared across two groups. In addition, student emotion was measured using a positive affect scale and biofeedback instruments. The results showed that PED group outperformed NED group on retention and transfer tests. However, insignificant difference was found between two groups on student emotion. Our results suggest that, although the effect of emotional design was insignificant on student emotion, it was significant on student learning performance (i.e. on retention and transfer tests). Based on our results, several implications were drawn and suggestions were made for educators and researchers.

Emotional DesignResearch Papers

Emotional Design in Multimedia Learning: How Emotional Intelligence Moderates Learning OutcomesJeya Amanth · 2016This study is designed as a preliminary study to explore the effects of emotional intelligence (EI) on achievement, perceived intrinsic motivation and perceived satisfaction when expose to an emotional designed Multimedia Learning Environment (MLE) that was designed to induce either positive, neutral or negative emotions. All three designs had similar content and narration but differed in visual element such as colour, font size, font style and images. Based on the findings, it was reported that students performed better in the design used to induce negative emotion (NegD design) followed by the positive (PosD) and Neutral (NeuD). There is no significant difference in levels of emotional intelligence towards these learning outcomes; however, students with Low EI performed better overall. EI only qualified perceived satisfaction when using a MLE designed to induce emotions and it was found that students with Low EI preferred the design that induces positive emotions. In addition, High EI students favored designs with emotionality (positive or negative) compared to neutral design. Index Terms—Emotional Design, Engineering Education, Emotional Intelligence, Multimedia- Based Learning, Aesthetics.

Emotional DesignResearch Papers

Positive Psychology and Digital Games: The Role of Emotions and Psychological Flow in Serious Games DevelopmentAndreas Alexiou, Michaéla Schippers et al. · 2012In this paper we will discuss how positive psychology can contribute to the design of digital games and in particular training applications like Serious Games. While digital games have been known for their ability to deeply immerse users, stimulate the senses and tap into a broad range of emotions, it has proven rather challenging to use them as a vehicle for pedagogy. Relevant research is still at its infancy and many of the mechanisms that foster knowledge creation and enhance learning need to be mapped out before scripted in the game. The theory of psychological flow and the role of positive emotions in broadening people's thought-action repertoires bring some practical insights and pave the path for tackling some important design questions. Yet there are still major challenges and uncharted waters to be explored in order for the technology to deliver what has been promised.

Emotional DesignResearch Papers

Full-immersion virtual reality for experiential education: An exploratory user experience analysisChristian Schott, Stephen Marshall · 2021Experiential education is widely considered an effective pedagogy to foster learning for our rapidly changing world. Despite this, residential fieldtrips are on the decline. Recent advances in full-immersion virtual reality (VR) technology offer great potential to make situated experiential education, such as fieldtrips, more accessible to educational institutions, however, research on VR technology's effectiveness in this context is lacking. This article documents exploratory action research which examines the effectiveness of full-immersion VR technology for experiential education by adopting a user experience (UX) analytical frame. Six university staff and five students who participated in a trial of a virtual environment developed for sustainable tourism education, discussed their user experience through semi-structured interviews. The UX lens which distinguished between three UX facets during the analysis, *beyond the instrumental, emotion and affect*, *experiential*, serves to identify research areas requiring attention and assists in the technology's improvement prioritisation. Interviews revealed many positive perspectives thus lending support to VR technology's suitability to foster experiential education, however, several negative experiences were also identified; principally motion sickness. The exploratory findings suggest that more research is warranted to more comprehensively examine VR technology's capacity to foster experiential education and locate VR's place in the education landscape.

HCI & Adaptive InterfacesResearch Papers

Article Methodologies of Learning Served by Virtual Reality: A Case Study in Urban InterventionsRicardo Torres-Kompen, David Fonsec · 2019A computer-simulated reality and the human-machine interactions facilitated by computer technology and wearable computers may be used as an educational methodology that transforms the way students deal with information. This turns the learning process into a more participative and active process, which fits both the practical part of subjects and the learner’s profile, as students nowadays are more technology-savvy and familiar with current technological advances. This methodology is being used in architectural and urbanism degrees to support the design process and to help students visualize design alternatives in the context of existing environments. This paper proposes the use of virtual reality (VR) as a resource in the teaching of courses that focus on the design of urban spaces. A group of users—composed of architecture students and professionals related to the architecture field—participated in an immersing VR experience and had the opportunity to interact with the space that was being redesigned. Later, a quantitative tool was used in order to evaluate the effectiveness of virtual systems in the design of urban environments. The survey was designed using as a reference the competences required in the urbanism courses; this allowed the authors to identify positive and negative aspects in an objective way. The results prove that VR helps to expand digital abilities in complex representation and helps users in the evaluation and decision-making processes involved in the design of urban spaces.

HCI & Adaptive InterfacesResearch Papers

Virtual and Augmented Reality in Architectural Design and EducationJulie Milovanovic, Guillaume Moreau et al. · 2017Virtual Reality and Augmented Reality research in the architecture field show a variety of possible uses of systems to accompany designers, laymen and decision makers in their architectural design process. This article provides a survey of VR and AR devices among a corpus of papers selected from conferences and journals on CAAD (Computer Aided Architectural Design). A closer look at some specific research projects highlights their potentials and limits, which formalize milestones for future challenges to address. Identifying advantages and drawbacks of those devices gave us insights to propose an alternative type of system, CORAULIS, including both VR and SAR technologies, in order to support collaborative design to be implemented in a pedagogical environment.

HCI & Adaptive InterfacesResearch Papers