Towards hybrid <scp>human‐AI</scp> learning technologies

Molenaar, Inge

doi:10.1111/ejed.12527

Cited by 50 publications

(30 citation statements)

References 79 publications

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“…Previous literature on the integration of GAI tools in education, especially by international scholars, emphasizes the potential of learner‐facing opportunities to enhance students' learning experiences through increased motivation, active participation, and individualized support (Cooper, 2023; Molenaar, 2022; Okaibedi, 2023). They highlight the potential of these tools to increase student motivation and engagement through interactive and personalized learning experiences, facilitate active participation by providing real‐time feedback, and adapt to individual student needs and learning styles.…”

Section: Discussionmentioning

confidence: 99%

Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools

Liu,

Ren,

Nyagoga

et al. 2023

Future in Educational Research

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ChatGPT is an artificial intelligence chatbot that utilizes advanced natural language processing technologies, including large language models, to produce human‐like responses to user queries spanning a wide range of topics from programming to mathematics. As an emerging generative artificial intelligence (GAI) tool, it presents novel opportunities and challenges to the ongoing digital transformation of education. This article employs a systematic review approach to summarize the viewpoints of Chinese scholars and experts regarding the implementation of GAI in education. The research findings indicate that a majority of Chinese scholars support the cautious integration of GAI into education as it serves as a learning tool that offers personalized educational experiences for students. However, it also raises concerns related to academic integrity and the potential hindrance to students' critical thinking skills. Consequently, a framework called DATS, which outlines an optimization path for future GAI applications in schools, is proposed. The framework takes into account the perspectives of four key stakeholders: developers, administrators, teachers, and students.

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Section: Discussionmentioning

confidence: 99%

Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools

Liu,

Ren,

Nyagoga

et al. 2023

Future in Educational Research

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“…10 Reviews in this area describe chatbots as educational resources growing in popularity and functionality 19−21 because more students have access to the Internet at home and at school, there is greater availability of intelligent learning technologies, and due to the digitalization of education accelerated by school closures due to the COVID-19 pandemic. 22 However, with this emerging resource, research gaps exist as there are few empirical studies investigating effective learning designs or learning strategies for this technology. 23…”

Section: ■ Introductionmentioning

confidence: 99%

“…Despite this limitation, chatbots have been used in diverse and creative ways, such as being learning partners, to support exam preparation, to answer frequently asked questions, , and as virtual students with misconceptions to support an authentic, meaningful, and open-ended teaching situation with preservice teachers . Reviews in this area describe chatbots as educational resources growing in popularity and functionality − because more students have access to the Internet at home and at school, there is greater availability of intelligent learning technologies, and due to the digitalization of education accelerated by school closures due to the COVID-19 pandemic . However, with this emerging resource, research gaps exist as there are few empirical studies investigating effective learning designs or learning strategies for this technology …”

Section: Introductionmentioning

confidence: 99%

Investigating the Use of an Artificial Intelligence Chatbot with General Chemistry Exam Questions

Clark

2023

J. Chem. Educ.

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The artificial intelligence chatbot ChatGPT was used to answer questions from final exams administered in two general chemistry courses, including questions with closed-response format and with open-response format. For closed-response questions, ChatGPT was very capable at identifying the concept even when the question included a great deal of chemical symbolism. However, ChatGPT’s success at solving problems was only 44%, a value well below the class average of 69%. On open-response questions, ChatGPT’s responses displayed strong language processing ability with higher performance on questions that could be solved with more generalizable information compared to questions that required specific skills, especially when those topics or skills were primarily found in lecture. Incorrect responses and flawed explanations were often logically sound and would be persuasive to a novice. The chatbot is currently ill-equipped to provide reliable answers or explanations to students for many representative exam questions, but a potential use is to create assignments in which students analyze and improve ChatGPT’s responses.

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“…Our findings show that teachers want to retain control when working with AI to co-orchestrate dynamic transitions. They want Pair-Up to supply suggestions and data, but teachers wish to retain the ultimate say over pedagogical decisions related to dynamic pairing (Molenaar, 2022). Additionally, teachers wanted Pair-Up to be accurate, flexible and customisable to support their classroom decisions (Shneiderman, 2022).…”

Section: Discussionmentioning

confidence: 99%

How teachers conceptualise shared control with an AI co‐orchestration tool: A multiyear teacher‐centred design process

Lawrence,

Echeverria,

Yang

et al. 2023

Brit J Educational Tech

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Artificial intelligence (AI) can enhance teachers' capabilities by sharing control over different parts of learning activities. This is especially true for complex learning activities, such as dynamic learning transitions where students move between individual and collaborative learning in un‐planned ways, as the need arises. Yet, few initiatives have emerged considering how shared responsibility between teachers and AI can support learning and how teachers' voices might be included to inform design decisions. The goal of our article is twofold. First, we describe a secondary analysis of our co‐design process comprising six design methods to understand how teachers conceptualise sharing control with an AI co‐orchestration tool, called Pair‐Up. We worked with 76 middle school math teachers, each taking part in one to three methods, to create a co‐orchestration tool that supports dynamic combinations of individual and collaborative learning using two AI‐based tutoring systems. We leveraged qualitative content analysis to examine teachers' views about sharing control with Pair‐Up, and we describe high‐level insights about the human‐AI interaction, including control, trust, responsibility, efficiency, and accuracy. Secondly, we use our results as an example showcasing how human‐centred learning analytics can be applied to the design of human‐AI technologies and share reflections for human‐AI technology designers regarding the methods that might be fruitful to elicit teacher feedback and ideas. Our findings illustrate the design of a novel co‐orchestration tool to facilitate the transitions between individual and collaborative learning and highlight considerations and reflections for designers of similar systems. Practitioner notesWhat is already known about this topic: Artificial Intelligence (AI) can help teachers facilitate complex classroom activities, such as having students move between individual and collaborative learning in unplanned ways. Designers should use human‐centred design approaches to give teachers a voice in deciding what AI might do in the classroom and if or how they want to share control with it. What this paper adds: Presents teacher views about how they want to share control with AI to support students moving between individual and collaborative learning. Describes how we adapted six design methods to design AI features. Illustrates a complete, iterative process to create human‐AI interactions to support teachers as they facilitate students moving from individual to collaborative learning. Implications for practice: We share five implications for designers that teachers highlighted as necessary when designing AI‐features, including control, trust, responsibility, efficiency and accuracy. Our work also includes a reflection on our design process and implications for future design processes.

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Towards hybrid human‐AI learning technologies

Cited by 50 publications

References 79 publications

Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools

Future of education in the era of generative artificial intelligence: Consensus among Chinese scholars on applications of ChatGPT in schools

Investigating the Use of an Artificial Intelligence Chatbot with General Chemistry Exam Questions

How teachers conceptualise shared control with an AI co‐orchestration tool: A multiyear teacher‐centred design process

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