Lessons Learned for AI Education with Elementary Students and Teachers

Ottenbreit-Leftwich, Anne; Glazewski, Krista; Jeon, Minji; Jantaraweragul, Katie; Hmelo‐Silver, Cindy E.; Scribner, Adam; Lee, Seung; Mott, Bradford W.; Lester, James C.

doi:10.1007/s40593-022-00304-3

Cited by 29 publications

(6 citation statements)

References 32 publications

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“…There is currently limited research on the application of generative AI in teaching practice due to that the implementation of it in education was implemented at the end of 2022 [ 16 ]. Anne et al summarized the ideas of primary school students and teachers regarding the use of AI in their research [ 17 ]. It is reported by Gwo-Jen Hwang that most of the studies on AI in nursing focused on evaluating the performance of AI tools and systems, and issues related to the cognition, affect, skills, and behaviors of the research subjects have been less frequently discussed [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study

Kong,

Ning,

et al. 2024

BMC Nurs

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Background Artificial intelligence is a growing phenomenon that will soon facilitate wide-scale changes in many professions, and is expected to play an important role in the field of medical education. This study explored the realistic feelings and experiences of nursing undergraduates participating in different stages of artificial intelligence + project task driven learning, and provide a basis for artificial intelligence participation in nursing teaching. Methods We conducted face-to-face semi-structured interviews with nursing undergraduates participating in Nursing Research Course which adopts artificial intelligence + project task driven learning from a medical university in Ningxia from September to November 2023, to understand their experience of using artificial intelligence for learning and the emotional changes at different stages. The interview guide included items about their personal experience and feelings of completing project tasks through dialogue with artificial intelligence, and suggestions for course content. Thematic analysis was used to analyze interview data. This study followed the COREQ checklist. Results According to the interview data, three themes were summarized. Undergraduate nursing students have different experiences in participating in artificial intelligence + project task driven learning at different stages, mainly manifested as diverse emotional experiences under initial knowledge deficiency, the individual growth supported by external forces during the adaptation period, and the expectations and suggestions after the birth of the results in the end period. Conclusions Nursing undergraduates can actively adapt to the integration of artificial intelligence into nursing teaching, dynamically observe students’ learning experience, strengthen positive guidance, and provide support for personalized teaching models, better leveraging the advantages of artificial intelligence participation in teaching.

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Section: Discussionmentioning

confidence: 99%

Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study

Kong,

Ning,

et al. 2024

BMC Nurs

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“…Educators may use this transparency to detect biases or faults in AI systems, enabling them to make essential improvements for accurate and fair decision-making. Transparency in AI decision-making is essential for building trust and enhancing the efficiency of AI systems in assisting teachers with their teaching methods (Ottenbreit-Leftwich et al, 2023).…”

Section: Ethical Considerations In Implementing Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence Helps Primary School Teachers to Plan and Execute Physics Classroom Experiments

Kotsis

2024

JETM

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The research claims that artificial intelligence technologies can help and direct primary school teachers in organising classroom experiments for physics instruction. Educators now have the potential to construct experimental projects that are entertaining and efficient, all while catering to their students’ many learning styles and capabilities. This is made possible by the availability of artificial intelligence technologies. The incorporation of artificial intelligence into educational settings may result in an improvement in the overall quality of teaching as well as an improvement in the scientific performance of students. The chance to improve the learning experience for both students and teachers is available to educators who do an in-depth study on artificial intelligence-driven teaching solutions. The research highlights how artificial intelligence can transform teaching approaches in elementary school, notably in the field of physics education within the context of primary school settings.

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“…Xia et al [17] suggested that both teachers' and students' attributes can influence students' achievement levels in AI learning from a self-determination theoretical perspective. Moreover, most of the existing literature focuses on the context of primary and secondary education, such as AI education in K-12 [18][19][20], AI education for elementary students [21][22][23], and AI education for middle school [24][25][26], while little attention and effort have been devoted to exploring undergraduates' perceptions and acceptance of AI education [27][28][29].…”

Section: Literature Review 21 Artificial Intelligence (Ai) In Online ...mentioning

confidence: 99%

The Role of Artificial Intelligence Autonomy in Higher Education: A Uses and Gratification Perspective

Niu,

Zhang,

Zhang

et al. 2024

Sustainability

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With the rapid development of artificial intelligence (AI) technology, AI educators have become a reality. The advancement and increasing applications of AI technology in higher education not only provide more efficient tools for teachers in long-term and focused teaching, but also provide new active and independent spaces for sustainable self-motivated learning for college students. It is of great importance that the effects of AI educator design are understood to ensure the sustainable development and deployment of AI-driven courses at universities. This paper investigates the influences of AI educators’ autonomy design on students’ usage intentions by delving into how the artificial autonomy of AI educators satisfies students’ needs. Drawing on the uses and gratification (U&G) framework, we theoretically elaborate on how AI educator autonomy (i.e., sensing autonomy, thought autonomy, and action autonomy) influences students’ intentions to use an AI educator through the mediating effects of U&G benefits (i.e., information-seeking gratification, social interaction gratification, and entertainment gratification). By conducting an online survey (N = 673) on college students, we found that the sensing autonomy of AI educators is positively associated with usage intention due to the mediating effects of social interaction and entertainment gratifications; the thought autonomy of AI educators is positively related to usage intention, mediated by information-seeking and social interaction gratifications, and the action autonomy of AI educators is positively linked with usage intention through the paths of information-seeking and entertainment gratifications. Our findings provide both theoretical contributions and practical implications.

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Lessons Learned for AI Education with Elementary Students and Teachers

Cited by 29 publications

References 32 publications

Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study

Experience of undergraduate nursing students participating in artificial intelligence + project task driven learning at different stages: a qualitative study

Artificial Intelligence Helps Primary School Teachers to Plan and Execute Physics Classroom Experiments

The Role of Artificial Intelligence Autonomy in Higher Education: A Uses and Gratification Perspective

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