2022
DOI: 10.3389/frai.2021.713176
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Keep Calm and Do Not Carry-Forward: Toward Sensor-Data Driven AI Agent to Enhance Human Learning

Abstract: The integration of Multimodal Data (MMD) and embodied learning systems (such as Motion Based Educational Games, MBEG), can help learning researchers to better understand the synergy between students' interactions and their learning experiences. Unfolding the dynamics behind this important synergy can lead to the design of intelligent agents which leverage students' movements and support their learning. However, real-time use of student-generated MMD derived from their interactions with embodied learning system… Show more

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Cited by 12 publications
(13 citation statements)
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“…To gain more objective evidence and holistic understanding, future work should use multimodal data (the fusion of information extracted from multiple data sources, such as eye-tracking, EEG, facial data streams, physiological indexes, etc.). For example, data sources from self-report-scale and eye-tracking during learners' interaction with a learning system can be integrated and used to measure cognitive load (Sharma et al, 2022 ).…”
Section: Discussionmentioning
confidence: 99%
“…To gain more objective evidence and holistic understanding, future work should use multimodal data (the fusion of information extracted from multiple data sources, such as eye-tracking, EEG, facial data streams, physiological indexes, etc.). For example, data sources from self-report-scale and eye-tracking during learners' interaction with a learning system can be integrated and used to measure cognitive load (Sharma et al, 2022 ).…”
Section: Discussionmentioning
confidence: 99%
“…Though more general research has investigated the role of AI in enhancing human learning (Sharma et al , 2022), for example, how generative AI might accelerate human learning (Johnson, 2023), manufacturing-specific examples are lacking. Just as Sakichi Toyoda developed Jidoka over 100 years ago, introducing a form of intelligence to mechanical automation to drive improvement and learning throughout the firm, the future developments of AI in lean manufacturing should consider how such technology can be used to promote learning and improvement within and across organizations.…”
Section: Discussionmentioning
confidence: 99%
“…Given the emergent nature of the research topic, rapid literature review (RLR) was selected to investigate the current and future application areas for AI in lean manufacturing. RLR is an alternative to systematic literature review (SLR) that can speed up the analysis of newly published data (Smela et al, 2023). To ensure that the search process was effective and efficient in producing results in a timely manner, only the research database Scopus was used (according to Bjørnbet et al (2021), Scopus covers a satisfactory share of relevant, extant literature and produces less noise, compared to other databases).…”
Section: Methodsmentioning
confidence: 99%
“…This learnercentered approach raises further discussion of how LA and MMLA might aid in the creation of customized multisensory learning experiences. Although such data can enable the use of automated feedback (e.g., errors correction, hints, or feedback provision) [94] and adaptations to support effective combinations of stimuli and interaction [95], [96], [97], [98], this potential remains unexplored in MSEs.…”
Section: B the Potential Of Mmla In Msesmentioning
confidence: 99%