Renovation of education requires students to engage in a newer style of learning, or collaborative learning and teachers to implement lesson study of that learning. Such learning, however, creates a situation wherein multiple student groups simultaneously engage in dialogues in a noisy classroom. Thus, we developed a “Learning Recorder” system, which has a 360-degree video camera to record students when set in the center of the group, and sends the voice data to an Automatic Speech Recognition system for immediate transcription. This paper explains how this system has evolved from our practical experiences with AI-powered lesson study and how it helped even a novice teacher look back and learn from the student dialogue. We propose that an information appliance like the Learning Recorder draws teachers’ attention to student learning, solicits multiple interpretations, and brings about collaborative learning among teachers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automated essay scoring (AES) is to estimate the scores of essays automatically. Two types of AES models are commonly used: handcrafted feature-based and neural-based models. In this paper, we introduce AES systems based on the two types for evaluating the logical consistency of Japanese essays. In addition, to enhance the performance of models, we integrate the neural-based model with the handcrafted features: a hybrid AES system. In the experiment, we show the effectiveness of our hybrid AES system. Besides, most of our AES models obtained higher QWK scores than human evaluators. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Psychomotor learning is an emerging research direction in the AIED (Artificial Intelligence in Education) field. This topic was introduced in the AIED research agenda back in 2016 in a contribution at the International Journal of AIED, where the SMDD (Sensing-Modelling-Designing-Delivering) process model to develop AIED psychomotor systems was introduced. Recently, a systematic review of the state of the art on this topic has also been published in the novel Handbook of AIED. In this context, the aim of the IPAIEDS tutorial is to motivate the AIED community to research on intelligent psychomotor systems and give tools to design, build and evaluate this kind of systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

While race and gender academic disparities have often been categorized via differences in final grade performance, the day-to-day experiences of minoritized student populations may not be accounted for when only concentrating on final grade outcomes. However, more fine-grained information on student behavior analyzed using AI and machine learning techniques may help to highlight the differences in day-to-day experiences. This study explores how linguistic features related to exclusion and social dynamics vary across discussion forum structures and how the variation depends on race and gender. We applied linear mixed-effect analysis to discussion posts across six semesters to investigate the effect of discussion forum structure, race, and gender on linguistic features. These results can be used to suggest design changes to instructors’ online discussion forums that will support students in feeling included. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Nurturing a sense of belonging in a classroom can positively impact student attrition, especially for underrepresented groups. In this work, we develop and study the effectiveness of a recommendation system that aims to foster belonging by sharing challenges and possible solutions that other students have had while taking the same course. Our system uses sentence transformers and calculates the similarity between students’ reflections. We measure participating students’ sense of belonging before and after they view the top 5 challenges and solutions received from other students. Using a significance level of 0.05, we found a p-value of 0.0145, indicating that there is a significant increase in overall belonging values. Students also rated 61% of the solutions as useful. This work allows future students to also benefit from the experiences of those before them. By showing students that those before them also had similar challenges and overcame them, we can show students that they do, in fact, belong among their peers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

In the proposed research, we investigated whether the standardized neuropsychological tests commonly used to assess attention can be used to measure students’ engagement in online learning settings. Accordingly, we employed 73 students in three clinically relevant neuropsychological tests to assess three types of attention. Students’ engagement performance, as evidenced by their facial video, was also annotated by three independent annotators. The manual annotations observed a high level of inter-annotator reliability (Krippendorffs’ Alpha of 0.864). Further, by obtaining a correlation value of 0.673 (Spearmans’ Rank Correlation) between manual annotation and neuropsychological tests score, our results show construct validity to prove neuropsychological test scores’ significance as a latent variable for measuring students’ engagement. Finally, using non-intrusive behavioral cues, including facial action unit and eye gaze data collected via webcam, we propose a machine learning method for engagement analysis in online learning settings, achieving a low mean squared error value (0.022). The findings suggest a neuropsychological test-based machine learning technique could effectively assess students’ engagement in online education. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Discussion summaries have been considered important for promoting peer interactions in online discussion forums. However, most previous research only focused on student-generated summaries and their impact on learners in the summarizing roles. Even though recent artificial intelligence (AI) progress has demonstrated the possibility of generating decent forum post summaries, little attention has been paid to the educational implications of such auto-generated forum post summaries. To further understand the perceptions and reactions of students to AI-generated forum summaries, this research used a Wizard of Oz approach to collect students’ online discussion forum log data with and without the “AI-generated” summaries. The results indicated that making an auto-generated summary available for students might not necessarily boost their interactions and engagements, especially for inactive students. However, the summary could serve as a reminder for students to participate in the discussion forum in a timely manner. Future research is needed to investigate whether the timing of providing an auto-generated summary might influence its impact. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Intelligent tutoring systems (ITSs) aim to support student learning through comprehensive adaptive features, making for costly development times—about 200–300 hours of development time per hour of instruction. This proposal outlines plans to overcome several technical challenges toward building authoring tools whereby a non-programmer can build ITSs by interactively teaching simulated students. I propose both interaction design considerations and machine-learning innovations. These include a multi-modal natural language processing mechanism that mimics student learning from narrated tutorial instruction, and an active-learning mechanism that identifies training examples likely to eliminate inaccuracies in the simulated student’s induced production rules. I propose to evaluate these features over 3 user studies and evaluate the generality of this authoring method in a final open-ended authoring study. This work aims to democratize ITS authoring by opening new authoring opportunities to non-programmers by making authoring as time-efficient and natural as human-to-human tutoring. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Computer-based learning environments (CBLEs) are often used to provide customized learning experiences for students. To enhance their effectiveness, we propose analyzing learners’ actions within CBLEs. In this study, we focus on the influence of learner mindset (fixed vs growth) on interaction patterns in a CBLE designed for teaching Python programming. Learner mindset refers to their beliefs about the malleability of their abilities. Individuals with a fixed mindset believe that their abilities are fixed, while those with a growth mindset believe abilities can be developed through learning. Using log data and pattern-mining techniques, we will identify learners’ interaction patterns and behaviour while also assessing their mindset through a questionnaire. We will compare the interaction patterns of fixed and growth mindset learners using task models to support our findings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

GCFGlobalLearning is a non-profit organization committed to creating life-changing opportunities through an innovative virtual education project that incorporates technological tools. Our platform offers free courses to learners worldwide, available in English, Spanish, and Portuguese. Since our launch, we have welcomed millions of learners. Our current focus is on incorporating artificial intelligence tools that will enhance our learners’ experience. To achieve this, we have developed a recommender system and a learning content search and organization tool that personalizes our learners’ learning journey. Even with limited information about our learners, we can enhance their experience through the use of these AI tools. In this paper, we introduce the platform’s primary components, detail how we overcome our limited learner information scenario, and share our vision of incorporating more artificial intelligence innovations in the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.