What Does an Emerging Intelligence Augmentation Economy Mean for HF/E? Can Learning Engineering Help?

Goodell, Jim; Craig, Scotty D.

doi:10.1177/1071181322661283

Cited by 3 publications

(1 citation statement)

References 22 publications

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Learning engineering is a transdisciplinary area focusing on the systematic application of evidence-based principles from science of learning disciplines to create effective learning experiences, addressing the challenges of learners. While learning engineering was first proposed in 1967 as a method for improving the basic efficiency for learning organizations (Simon, 1967), the professional practice of the area has just started forming (Goodell & Craig, 2022) and basic principles of the area are summarized in a book covering the discipline, Learning Engineering Toolkit . The emerging area uses skills from multiple disciples to tackle educational issues (e.g., scaling-up online learning) that are too large or complicated to be solved by one skillset (Kessler et al 2023).

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confidence: 99%

Learning Engineering Perspectives for Supporting Educational Systems

Craig,

Goodell,

Czerwinski

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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This panel will focus on the emerging area of Learning Engineering. Learning Engineering is a transdisciplinary area focusing on the systematic application of evidence-based principles from science of learning disciplines to create effective learning experiences, addressing the challenges of learners. During the panel, examples of Learning Engineering will be presented of interest to anyone within human factors and ergonomics with interest in education, training, or usability/design science. The panel will represent experience from both academia and industry. The goal of this panel is to foster dialog between the IEEE Industry Connections Industry Consortium on Learning Engineering (ICICLE) and HFES members in the hope of increasing knowledge of Learning Engineering and creating ties between the two organizations.

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confidence: 99%

Learning Engineering Perspectives for Supporting Educational Systems

Craig,

Goodell,

Czerwinski

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Interactive Peer Instruction Method Applied to Classroom Environments Considering a Learning Engineering Approach to Innovate the Teaching–Learning Process

Rivadeneira

Inga

2023

Education Sciences

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Faced with the problem of a need for more student participation and motivation in the teaching–learning process (TLP) due to the persistence of traditional methods, peer instruction (PI) has emerged as an interactive teaching method. It is based on a dynamic of questions and answers to promote student reflection and discussion. Thus, this article shows the applicability of PI, considering a learning engineering approach to innovating the TLP. For this, the historical-descriptive method is used to conduct a literature review and a bibliometric study, evaluating scientific articles in Web of Science (WoS) and Scopus between 2018 and 2022. In addition, in the second stage, the experimental method is used to apply PI in two educational institutions and evaluate its applicability with Likert scales for teachers and students following a quantitative methodology. Consequently, following the analytical-synthetic method, the results indicate that PI the stages, the most relevant aspects, and the conditions to consider in a classroom environment are highly relevant to enhancing its effects. Thus, such applicability is reflected in its positive results in the TLP, considering the learning engineering, and its representation as a flexible and innovative alternative to traditional methods. This is because PI generated benefits for teachers and students, thus encouraging greater satisfaction, motivation, interest, understanding, and student participation.

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Intelligence Augmentation via Human-AI Symbiosis

Stylos

2023

Advances in Human and Social Aspects of Technology

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Intelligence augmentation (IA) facilitates a new systems perspective to frame the value outcome of the interaction between human and AI agents. The factors that can optimize this collaborative integration of the multi-agent system are investigated and discussed. Different kinds of knowledge approaches are met in various contexts to create an optimized IA system in service settings. In this respect, AI agents are not just tools but rather co-creators of value that can influence human agents' learning cycles. Hence, humans' effective interaction with AI agents produces a learning effect that can empower humans' interpretative capability. This chapter focuses on IA and shows that IA is not only a theoretical paradigm but also serves as a platform to facilitate the transition from smart services to wise service innovation to the benefit of both the multi-agent system benefitting service organizations and the consumers too. Potential challenges are also discussed from a societal viewpoint.

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What Does an Emerging Intelligence Augmentation Economy Mean for HF/E? Can Learning Engineering Help?

Cited by 3 publications

References 22 publications

Learning Engineering Perspectives for Supporting Educational Systems

Learning Engineering Perspectives for Supporting Educational Systems

Interactive Peer Instruction Method Applied to Classroom Environments Considering a Learning Engineering Approach to Innovate the Teaching–Learning Process

Intelligence Augmentation via Human-AI Symbiosis

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