Pdf Human Ai Shared Regulation For Hybrid Intelligence In Learning And

Bonisiwe Shabane
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pdf human ai shared regulation for hybrid intelligence in learning and

Humanities and Social Sciences Communications volume 12, Article number: 821 (2025) Cite this article The integrating AI into teaching and learning has the potential to transform traditional classroom environments into hybrid intelligence learning environments, whereby human teachers and AI teachers (educational robots) work together synergistically to enhance students’... To understand and optimize the synergistic effect of human–AI collaboration in hybrid intelligence learning environments, this study proposes a human–AI synergy degree model (HAI-SDM). A case study was conducted to examine the synergy degree and order degree in human–AI collaboration, involving forty students and one teacher from a class in a junior high school. The results indicate that the order degree between human teacher and AI machines remains at a moderate level while undergoing dynamic changes. The synergy degree fluctuates between low and moderate, reflecting relatively orderly development among the three subsystems (collaboration subject subsystem, collaboration process subsystem and collaboration environment subsystem), but one subsystem may exhibit disordered behaviours in...

These findings have implications for developing more effective human-AI classroom collaboration and promoting the effective integration of AI into teaching and learning. The rapid development of artificial intelligence (AI) technology has brought unprecedented opportunities and challenges to the education sector. Particularly, under the impetus of human–AI collaboration, AI not only serves as an auxiliary tool to support teachers’ instructional tasks but also actively participates in classroom interactions in an intelligent manner, facilitating a profound... 2024; Zhou and Hou, 2024; Hilpert et al. 2023). Traditional educational models have limitations in facilitating personalized learning and optimizing resource allocation.

Human–AI collaborative teaching offers an efficient and sustainable lens to address these challenges through close collaboration between AI systems and teachers (Díaz and Nussbaum, 2024; Chen et al. 2022). Human-AI collaboration in education, as an emerging interdisciplinary field, integrates cutting-edge theories and technologies from AI, education, cognitive science, and human–computer interaction. The Hybrid Intelligence Learning Environments design aims to develop and implement effective human–AI collaboration in education. Its core philosophy lies in the seamless integration of human intelligence and machine intelligence to achieve optimized teaching outcomes through their synergistic collaboration (Cukurova, 2024; Bredeweg and Kragten, 2022). Within this environment, AI not only functions as an assistant to teachers but also plays a vital role in personalized learning (Mittal et al.

2024), real-time feedback (Weber et al. 2024), cognitive intervention (Fan et al. 2024), and emotional engagement (Järvelä et al. 2023). In recent years, researchers started to look into the design of educational robots, the application of AI in teaching, and the cognitive and behavioural dynamics of human–AI collaboration (Schecter et al. 2022; Niu et al.

2024; Wu et al. 2024). Existing studies have demonstrated significant advantages of human–AI collaboration in enhancing learning efficiency and supporting personalized learning (Huang et al. 2021). For instance, the integration of AI applications in real-time question answering (Fang et al. 2023) and homework grading (Duan et al.

2023) has effectively reduced teachers’ workload while improved instructional quality. The most extensive literature on AI applications in education focused on technological aspects, lacking systematic examination of human-AI collaboration during classroom instruction (Vössing et al. 2022; Yue and Li, 2023). Given that effective human-AI collaboration relies on effective collaboration between humans and AI systems, investigating the effectiveness of collaboration and the degree of synergy in classrooms becomes crucial. To address this research gap, this study aims to develop a framework to evaluate and enhance the synergy and orderliness of human–AI collaboration in classrooms. Socially shared regulation of learning (SSRL) is a crucial process for groups of learners to successfully collaborate.

Detecting and supporting SSRL is a challenge, especially in real time, but hybrid intelligence approaches such as Artificial Intelligence (AI) agents may make this possible. Leveraging the concept of trigger events which invite SSRL, we present a design of an AI agent, MAI, which can detect SSRL and prompt students to raise their group‐level metacognitive awareness with the aim... We present the methodology we used to design MAI, drawing on the Echeloned DSR (eDSR) Methodological Framework and making use of the Wizard of Oz prototyping paradigm. We likewise present empirical results evaluating our initial prototype of MAI, using lexical alignment between speakers as a quantitative measure of the effect of MAI's prompts on facilitating SSRL, the Partner Model Questionnaire as... We found that the first prototype of MAI did not facilitate SSRL as hoped, possibly owing to mixed perceptions of MAI's reliability and lack of clarity about MAI's role in the collaborative learning task. From these findings, we offer revised prompts for the next iteration of prototyping this agent and a refined set of design requirements for future development of metacognitive AI agents for supporting SSRL.

Socially Shared Regulation of Learning (SSRL) is recognized as a critical component for the success of collaborative learning, emphasizing the importance of group‐level regulatory processes in achieving shared goals, enacting strategies and monitoring learning... Supporting SSRL in face‐to‐face collaborative learning environments presents challenges, including the complexity of coordinating and synchronizing individual contributions and regulatory actions within a group context. This paper introduces the design of Metacognitive Artificial Intelligence (MAI), a novel AI system aimed at enhancing Human‐AI collaboration for supporting and augmenting SSRL processes. Through empirical research, the study offers lessons learned and design considerations for developing artificial agents on facilitating and enhancing SSRL among learners, demonstrating how AI can play a pivotal role in collaborative learning environments. > endobj 27 0 obj <> endobj 28 0 obj <>/Font<>/ProcSet[/PDF/Text]>>/Rotate 0/Thumb 17 0 R/Type/Page>> endobj 29 0 obj <> endobj 30 0 obj <> endobj 31 0 obj <> endobj 32 0 obj... TL2]����晀jq�tC�h�ՉR�q��+bE]B�m/���æS�������p����xƜ��A���#!�G�$�𱸀񉈘F��=����a�mI E8+�L M1�D�n���&|6��d���-��2�����DB�h< :e���ۍ�̠��`�D)%������)l}y��W��㛁�� �}���.��S�0�d:�� 3s�SD��zx�h\�+"z:��U�ff ��v����z 3�s* �R���X�@�}��� ����uI��������j~^�Cb.�4�g;;�P2��h�� �* T@��(��t�B� ��>�r��>ɐN[_/�A��p�?�l1�l��S�6f�t��к]��XQ����H��Hi\<8�~݂����cN����HDW�Q]��Ű��o � |�EB!�� ̱ L�iv*H�,�jX-��r?x���.\\�PX��K��\�� @�D�c� �Ia�$#s��N�c?P- �#< `{��'�F|����A�ӭ�~GX�B�)�% �گ�M���8��3i��!�:�u�'@�����ٓv��$�0L�#�>�,�oR(K�De��D�Ϥ������Bm��T77�5z��@����H�����,Tm����Мs��O ��׎Mz]6��"o�n.Vא��/`y���F�́�<�/�,E�f8�}�o7�~z�[g��~̾ �C��b*�f��v}k��}hw���ߤ�i���/���|�_fd=T��'�„<�?�D�G|��D�,F����˛�#!R����9�B���`���u������Z�^/�֠š�� _�z��:�}�g��,�0w��I}����m3��E�B�k��H6-* ��>\�S�Q�|���?-��?/�� �����䟗皤ݭ/t;�N�A�A�I�� ݿ �R6���?���K���,�&/���l�:S�� ���V>�{3�S�[��r��p���U4P�*��Pf|(9��i�3��sID��˜(�b*��H{+�Az�����A|B��E�9h��2�4��9U��.u�~���?�����#[L��$o\���o� ���0���0`V��\��<�<��3�{x��}{��=��{�}h�����)h���"�� ��-�����՗�)�����!7�"{��Ob'Oy/�~#�z�ut񽭗_ڄ�^ȿ�T�a�����*�"�x��b��>���ԥ-��/F*|��FO<�O-x[t|�"m����}N8�g!6� �b�"���B�b*��)%��r���M����` ��v(�����:�����bk����j��?��ڶ%�Jv-����i����M���'���1���������������...

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Humanities and Social Sciences Communications volume 12, Article number: 821 (2025) Cite this article The integrating AI into teaching and learning has the potential to transform traditional classroom environments into hybrid intelligence learning environments, whereby human teachers and AI teachers (educational robots) work together synergistically to enhance students’... To understand and optimi...

These Findings Have Implications For Developing More Effective Human-AI Classroom

These findings have implications for developing more effective human-AI classroom collaboration and promoting the effective integration of AI into teaching and learning. The rapid development of artificial intelligence (AI) technology has brought unprecedented opportunities and challenges to the education sector. Particularly, under the impetus of human–AI collaboration, AI not only serves as an a...

Human–AI Collaborative Teaching Offers An Efficient And Sustainable Lens To

Human–AI collaborative teaching offers an efficient and sustainable lens to address these challenges through close collaboration between AI systems and teachers (Díaz and Nussbaum, 2024; Chen et al. 2022). Human-AI collaboration in education, as an emerging interdisciplinary field, integrates cutting-edge theories and technologies from AI, education, cognitive science, and human–computer interacti...

2024), Real-time Feedback (Weber Et Al. 2024), Cognitive Intervention (Fan

2024), real-time feedback (Weber et al. 2024), cognitive intervention (Fan et al. 2024), and emotional engagement (Järvelä et al. 2023). In recent years, researchers started to look into the design of educational robots, the application of AI in teaching, and the cognitive and behavioural dynamics of human–AI collaboration (Schecter et al. 2022; Niu et al.

2024; Wu Et Al. 2024). Existing Studies Have Demonstrated Significant

2024; Wu et al. 2024). Existing studies have demonstrated significant advantages of human–AI collaboration in enhancing learning efficiency and supporting personalized learning (Huang et al. 2021). For instance, the integration of AI applications in real-time question answering (Fang et al. 2023) and homework grading (Duan et al.