Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?

Markauskaitė, Lina; Marrone, Rebecca; Poquet, Oleksandra; Knight, Simon; Martínez-Maldonado, Roberto; Howard, Sarah; Tondeur, Jo; Laat, Maarten de; Shum, Simon Buckingham; Gašević, Dragan; Siemens, George

doi:10.1016/j.caeai.2022.100056

Cited by 103 publications

(46 citation statements)

References 57 publications

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“…The students' stream, in this case, bridged these concepts with shorter paths, indicating a higher semantic relatedness [17,14] of "work" and "maths"/"physics" compared to other pairs (like "math" and "fun" in students' BFMN). The semantic associations in both groups reflected domain knowledge expertise and suggested an awareness in both groups of the relevance of math-related skills in work settings, in agreement with recent expert-driven surveys (see [19]).…”

Section: Discussionsupporting

confidence: 87%

Introducing mindset streams to investigate stances towards STEM in high school students and experts

Brian¹,

Stella²

2023

Preprint

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We introduce mindset streams for assessing ways of bridging two target concepts in concept maps. We focus on behavioural forma mentis networks (BFMN), which map the associative and affective dimensions of memory recalls. Inspired by trains of thoughts taking several paths to link ideas, mindset streams are defined as BFMN subgraphs induced by all shortest paths between two target concepts, e.g. all recalls in shortest paths bridging "math" and "learning". These streams quantify the following features of the mindset encoded in a BFMN: (i) semantic content (i.e. which ideas mediate connections between targets?), (ii) valence coherence/conflict (i.e. are connections mediated by entwining ideas perceived negatively, positively or neutrally?), and (iii) semantic relevance (i.e. are the bridges between targets peripheral or central for the connectivity/betweenness of the BFMN?). We investigate mindset streams between "maths"/"physics" and key motivational aspects of learning ("fun","work","failure") in two BFMNs, encoding how 159 students and 59 experts perceived and associated concepts about Science Technology Engineering and Maths (STEM), respectively. Statistical comparisons against configuration models show that high schoolers bridge "maths" and "fun" only through overabundant levels of valence-conflicting associations, contrasting negatively perceived domain knowledge with peer-related positive experiences. This conflict is absent in the researchers' mindset stream, which rather bridges "math" and "fun" through positive, science-related associations. The mindset streams of both groups bridge "maths" and "physics" to "work" through mostly positive career-related jargon. Students' mindset streams of "failure" and "math"/"physics" are dominated by negative associations with test anxiety, whereas researchers integrate "failure" and "math"/"physics" in semantically richer and more positive contexts, denoting failure itself as a cornerstone of STEM learning. We discuss our findings and future research directions in view of relevant psychology/education literature.

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Section: Discussionsupporting

confidence: 87%

Introducing mindset streams to investigate stances towards STEM in high school students and experts

Brian¹,

Stella²

2023

Preprint

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“…We provide a further theoretical contribution through the provision of an evidence-based model to an ever-growing body of literature on AI in education. As highlighted in Markauskaite et al (2022), there are various capabilities that learners need to be prepared for a world with AI. These authors note the importance of developing capabilities alongside AI that are sustainable and which can be integrated into every part of the curriculum irrespective of the education environment.…”

Section: Discussionmentioning

confidence: 99%

Statistical modelling of the impact of online courses in higher education on sustainable development

Arango-Uribe

Barrera-Causil

Pallares

et al. 2022

IJSHE

Self Cite

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Purpose The concept of sustainable development (SD) is a popular response to society’s need to preserve and extend the life span of natural resources. One of the 17 goals of the SD is “education quality” (Fourth Goal of Sustainable Development [SDG-4]). Education quality is an important goal because education is a powerful force that can influence social policies and social change. The SDG-4 must be measured in different contexts, and the tools to quantify its effects require exploration. So, this study aims to propose a statistical model to measure the impact of higher education online courses on SD and a structural equation model (SEM) to find constructs or factors that help us explain a sustainability benefits rate. These proposed models integrate the three areas of sustainability: social, economic and environmental. Design/methodology/approach A beta regression model suggests features that include the academic and economic opportunities offered by the institution, the involvement in research activities and the quality of the online courses. A structural equation modelling (SEM) analysis allowed selecting the key variables and constructs that are strongly linked to the SD. Findings One of the key findings showed that the benefit provided by online courses in terms of SD is 62.99% higher than that of offline courses in aspects such as transportation, photocopies, printouts, books, food, clothing, enrolment fees and connectivity. Research limitations/implications The SEM model needs large sample sizes to have consistent estimations. Thus, despite the obtained estimations in the proposed SEM model being reliable, the authors consider that a limitation of this study was the required time to collect data corresponding to the estimated sample size. Originality/value This study proposes two novel and different ways to estimate the sustainability benefits rate focused on SDG-4, and machine learning tools are implemented to validate and gain robustness in the estimations of the beta model. Additionally, the SEM model allows us to identify new constructs associated with SDG-4.

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“…However, without addressing the issue of teachers’ roles and competencies, the claimed benefits could be questionable. Therefore, it is important to consider how teachers’ competencies change in an AI context (Markauskaite et al, 2022 ).…”

Section: Opportunities and Challenges Brought By The Pandemicmentioning

confidence: 99%

“…Teachers who know how to use AI may replace the teachers who do not know how because AI can empower teachers and promote their role transformation, which greatly improves the efficiency of management and the level of decision-making (Ng et al, 2022b ; Vazhayil et al, 2019 ). Markauskaite et al ( 2022 ) suggested that educators need to integrate new digital technologies and support learning to meet educational standards through digital technologies, engage in professional learning to build competencies, and gain experience using AI-enabled tools. Moreover, they should learn how to use appropriate AI-driven technologies such as adaptive learning systems and intelligent agents to facilitate their daily teaching management and practices to collaborate with different parties (e.g., parents, colleagues), enhance personalized learning to understand students’ learning progress and needs, and conduct various tasks such as offering automatic feedback, self-diagnosing, and promoting online collaboration among learners (Cavalcanti et al, 2021 ).…”

Section: Motivation For Teachers’ Ai Digital Competencymentioning

confidence: 99%

Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world

Leung

et al. 2023

Education Tech Research Dev

160

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The pandemic has catalyzed a significant shift to online/blended teaching and learning where teachers apply emerging technologies to enhance their students’ learning outcomes. Artificial intelligence (AI) technology has gained its popularity in online learning environments during the pandemic to assist students’ learning. However, many of these AI tools are new to teachers. They may not have rich technical knowledge to use AI educational applications to facilitate their teaching, not to mention developing students’ AI digital capabilities. As such, there is a growing need for teachers to equip themselves with adequate digital competencies so as to use and teach AI in their teaching environments. There are few existing frameworks informing teachers of necessary AI competencies. This study first explores the opportunities and challenges of employing AI systems and how they can enhance teaching, learning and assessment. Then, aligning with generic digital competency frameworks, the DigCompEdu framework and P21’s framework for twenty-first century learning were adapted and revised to accommodate AI technologies. Recommendations are proposed to support educators and researchers to promote AI education in their classrooms and academia.

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Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?

Cited by 103 publications

References 57 publications

Introducing mindset streams to investigate stances towards STEM in high school students and experts

Introducing mindset streams to investigate stances towards STEM in high school students and experts

Statistical modelling of the impact of online courses in higher education on sustainable development

Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world

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