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In this paper, I argue that the fields of artificial intelligence (AI) and education have been deeply intertwined since the early days of AI. Specifically, I show that many of the early pioneers of AI were cognitive scientists who also made pioneering and impactful contributions to the field of education. These researchers saw AI as a tool for thinking about human learning and used their understanding of how people learn to further AI. Furthermore, I trace two distinct approaches to thinking about cognition and learning that pervade the early histories of AI and education. Despite their differences, researchers from both strands were united in their quest to simultaneously understand and improve human and machine cognition. Today, this perspective is neither prevalent in AI nor the learning sciences. I conclude with some thoughts on how the artificial intelligence in education and learning sciences communities might reinvigorate this lost perspective.

Brain–computer interfaces (BCIs) are a form of technology that read a user’s neural signals to perform a task, often with the aim of inferring user intention. They demonstrate potential in a wide range of clinical, commercial, and personal applications. But BCIs are not always simple to operate, and even with training some BCI users do not operate their systems as intended. Many researchers have described this phenomenon as “BCI illiteracy,” and a body of research has emerged aiming to characterize, predict, and solve this perceived problem. However, BCI illiteracy is an inadequate concept for explaining difficulty that users face in operating BCI systems. BCI illiteracy is a methodologically weak concept; furthermore, it relies on the flawed assumption that BCI users possess physiological or functional traits that prevent proficient performance during BCI use. Alternative concepts to BCI illiteracy may offer better outcomes for prospective users and may avoid the conceptual pitfalls that BCI illiteracy brings to the BCI research process.

Family health history (FHx) is an important predictor of a person's genetic risk but is not collected by many adults in the United States. This study aims to test and compare the usability, engagement, and report usefulness of 2 web-based methods to collect FHx. This mixed methods study compared FHx data collection using a flow-based chatbot (KIT; the curious interactive test) and a form-based method. KIT's design was optimized to reduce user burden. We recruited and randomized individuals from 2 crowdsourced platforms to 1 of the 2 FHx methods. All participants were asked to complete a questionnaire to assess the method's usability, the usefulness of a report summarizing their experience, user-desired chatbot enhancements, and general user experience. Engagement was studied using log data collected by the methods. We used qualitative findings from analyzing free-text comments to supplement the primary quantitative results. Participants randomized to KIT reported higher usability than those randomized to the form, with a mean System Usability Scale score of 80.2 versus 61.9 (P<.001), respectively. The engagement analysis reflected design differences in the onboarding process. KIT users spent less time entering FHx information and reported more conditions than form users (mean 5.90 vs 7.97 min; P=.04; and mean 7.8 vs 10.1 conditions; P=.04). Both KIT and form users somewhat agreed that the report was useful (Likert scale ratings of 4.08 and 4.29, respectively). Among desired enhancements, personalization was the highest-rated feature (188/205, 91.7% rated medium- to high-priority). Qualitative analyses revealed positive and negative characteristics of both KIT and the form-based method. Among respondents randomized to KIT, most indicated it was easy to use and navigate and that they could respond to and understand user prompts. Negative comments addressed KIT's personality, conversational pace, and ability to manage errors. For KIT and form respondents, qualitative results revealed common themes, including a desire for more information about conditions and a mutual appreciation for the multiple-choice button response format. Respondents also said they wanted to report health information beyond KIT's prompts (eg, personal health history) and for KIT to provide more personalized responses. We showed that KIT provided a usable way to collect FHx. We also identified design considerations to improve chatbot-based FHx data collection: First, the final report summarizing the FHx collection experience should be enhanced to provide more value for patients. Second, the onboarding chatbot prompt may impact data quality and should be carefully considered. Finally, we highlighted several areas that could be improved by moving from a flow-based chatbot to a large language model implementation strategy.

PurposeThe PICCH research project contributes to opening a dialogue between cultural heritage archives and users. Hence, the users are identified and their information needs, the search strategies they apply and the search challenges they experience are uncovered.Design/methodology/approachA combination of questionnaires and interviews is used for collection of data. Questionnaire data were collected from users of three different audiovisual archives. Semi-structured interviews were conducted with two user groups: (1) scholars searching information for research projects and (2) archivists who perform their own scholarly work and search information on behalf of others.FindingsThe questionnaire results show that the archive users mainly have an academic background. Hence, scholars and archivists constitute the target group for in-depth interviews. The interviews reveal that their information needs are multi-faceted and match the information need typology by Ingwersen. The scholars mainly apply collection-specific search strategies but have in common primarily doing keyword searching, which they typically plan in advance. The archivists do less planning owing to their knowledge of the collections. All interviewees demonstrate domain knowledge, archival intelligence and artefactual literacy in their use and mastering of the archives. The search challenges they experience can be characterised as search system complexity challenges, material challenges and metadata challenges.Originality/valueThe paper provides a rare insight into the complexity of the search situation of cultural heritage archives, and the users’ multi-facetted information needs and hence contributes to the dialogue between the archives and the users.

Immersive Virtual Reality (IVR) consists of artificial computer-generated environments allowing a user to perceive the sensation of being present and interact in an ambience that convincingly replaces the physical world. When travel is restricted, such visualization power can be shared globally as an essential remote teaching tool for educational institutions through the Internet. The current advancements in IVR technology and their ubiquitous availability at affordable costs present a conducive environment for teaching and learning in both in-person and remote settings. The research presented in this paper explores the use of IVR technologies for teaching architectural history and presents tangible student learning outcomes. Specifically, the Pantheon in Rome was used as a representative test case for evaluating the effectiveness of IVR as a medium for remote teaching. Unlike Augmented Reality (AR), where virtual information is overlaid on physical real-world objects, this research focuses on IVR implementation and its effectiveness as a history teaching medium from exploring: (1) the nature of VR, (2) how IVR can be used online for teaching history, (3) the representation of IVR for presenting history, and (4) issues of learning outcomes. Two assessments with 57 and 68 students were separately conducted and five independent variables of: (1) learning about architecture, (2) history, (3) sense of presence in VR, (4) structural realism, and (5) comparison to in-class learning were evaluated using scores. Studies revealed that the intricate architectural details combined with high-resolution imagery and audio narrations for objects of historical interest in coordination with the user’s viewpoint within the IVR environment provided an excellent learning experience. The true past and the reality of history can be implemented in IVR through seeing objects and hearing historical data. Further, the use of IVR afforded the opportunity for students to accurately gauge, recognize, and appreciate the 3D aspects, size, and proportion of virtual spaces. The preparation of the Pantheon model, the development of an interactive IVR application, the design of the studies, the measurement of learning outcomes, and technological challenges are presented in this paper. Future exploration of new technologies to improve the representation of history by execution speed, which is the determining factor impacting realism of model and viewing experience, are also explained.

Human–computer interaction (HCI) is a research field which engages different disciplines, interest groups and communities, and which has emerged in different countries at different times. To understand how the HCI research community has evolved in Brazil, this paper applies data and visual analytics to its main conference series, the Brazilian Symposium on Human Factors in Computing Systems, henceforth IHC. We have explored the metadata of all 340 full papers published in the 14 editions of IHC. Our goal was to investigate the evolution of the Brazilian HCI community so we can raise the level of “self-knowledge” and thus discuss strategies that can further help develop this research community. From our analysis, we could understand more deeply the authorship profile of our community and how it has changed over time, the co-authorship networks evolution, the prominent institutions and states, the reference profile and the research topics over time. We hope that this paper will contribute to inspire other scientific communities to analyze themselves, and encourage their own discussions.

In this paper we present a new method for automatic transliteration and segmentation of Unicode cuneiform glyphs using Natural Language Processing (NLP) techniques. Cuneiform is one of the earliest known writing system in the world, which documents millennia of human civilizations in the ancient Near East. Hundreds of thousands of cuneiform texts were found in the nineteenth and twentieth centuries CE, most of which are written in Akkadian. However, there are still tens of thousands of texts to be published. We use models based on machine learning algorithms such as recurrent neural networks (RNN) with an accuracy reaching up to 97% for automatically transliterating and segmenting standard Unicode cuneiform glyphs into words. Therefore, our method and results form a major step towards creating a human-machine interface for creating digitized editions. Our code, Akkademia, is made publicly available for use via a web application, a python package, and a github repository.

Hereditary breast and ovarian cancer (HBOC) is a major type of hereditary cancer. Establishing effective screening to identify high-risk individuals for HBOC remains a challenge. We developed a prototype of a chatbot system that uses artificial intelligence (AI) for preliminary HBOC screening to determine whether individuals meet the National Comprehensive Cancer Network BRCA1/2 testing criteria. This study's objective was to validate the feasibility of this chatbot in a clinical setting by using it on a patient population that visited a hospital. We validated the medical accuracy of the chatbot system by performing a test on patients who consecutively visited the Kanagawa Cancer Center. The participants completed a preoperation questionnaire to understand their background, including information technology literacy. After the operation, qualitative interviews were conducted to collect data on the usability and acceptability of the system and examine points needing improvement. A total of 11 participants were enrolled between October and December 2020. All of the participants were women, and among them, 10 (91%) had cancer. According to the questionnaire, 6 (54%) participants had never heard of a chatbot, while 7 (64%) had never used one. All participants were able to complete the chatbot operation, and the average time required for the operation was 18.0 (SD 5.44) minutes. The determinations by the chatbot of whether the participants met the BRCA1/2 testing criteria based on their medical and family history were consistent with those by certified genetic counselors (CGCs). We compared the medical histories obtained from the participants by the CGCs with those by the chatbot. Of the 11 participants, 3 (27%) entered information different from that obtained by the CGCs. These discrepancies were caused by the participant's omissions or communication errors with the chatbot. Regarding the family histories, the chatbot provided new information for 3 (27%) of the 11 participants and complemented information for the family members of 5 (45%) participants not interviewed by the CGCs. The chatbot could not obtain some information on the family history of 6 (54%) participants due to several reasons, such as being outside of the scope of the chatbot's interview questions, the participant's omissions, and communication errors with the chatbot. Interview data were classified into the following: (1) features, (2) appearance, (3) usability and preferences, (4) concerns, (5) benefits, and (6) implementation. Favorable comments on implementation feasibility and comments on improvements were also obtained. This study demonstrated that the preliminary screening system for HBOC using an AI chatbot was feasible for real patients.