Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology

Jian, Huang; Shen, Gang; Ren, Xiuyan

doi:10.3991/ijet.v16i05.20287

Cited by 32 publications

(30 citation statements)

References 15 publications

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“…We identified the topics of investigation (see Figure 2) linking (i) the set of qualities that characterize the teachers' professional development challenges according to Stone Wiske et al ( 2001) and (ii) the constituent dimensions of a teaching system identified by Huang et al (2021).…”

Section: Summary Of the Systematic Review Processmentioning

confidence: 99%

“…The term "teaching content" refers to the body of knowledge and information that teachers teach and that students are expected to learn within a given domain of knowledge (Chassignol et al, 2018). According to Huang et al (2021), teaching content comprises the knowledge, skills, thoughts and behaviors transmitted by the school to students at all levels.…”

Section: Teaching Contentmentioning

confidence: 99%

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Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

et al. 2023

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Through a systematic analysis of the literature, this study analyzes the change in the teacher's role triggered by the integration of AI into educational systems. The picture offered by the systematic analysis of the literature conducted in this study reveals a less than total awareness of the urgency with which the challenges imposed by AI in the educational field must be addressed. We propose a manifesto to guide the evolution of the teachers' role according to the paradigm shift concept proposed by Kuhn in the scientific field.

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Section: Summary Of the Systematic Review Processmentioning

confidence: 99%

Section: Teaching Contentmentioning

confidence: 99%

Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

et al. 2023

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“…Meanwhile, students responded that there are some weaknesses in the system, which are related. (1) The inflexible structure (plot) of the "command" or conversation "as written in the manual" and the number of questions they should articulate was inconvenient (seven students). ( 2) The SR accuracy of the AI speaker was problematic, which led to the incorrect reaction of the system (e.g., not responding, playing pop music).…”

Section: System Usability Scale (Sus)mentioning

confidence: 99%

“…The innovative educational research and practices to follow up the rapidly changing contemporary world are now supported by Artificial Intelligence (AI) technologies [1]. The Natural Language Processing (NLP) technology has explosively grown due to the deep learning algorithms [2], which are relevant to converting human speech to text (STT; or speech recognition, SR) and text to human-like speech (text to speech, TTS; or speech generation).…”

Section: Introductionmentioning

confidence: 99%

Development of the Hands-free AI Speaker System Supporting Hands-on Science Laboratory Class

GyeongGeon

Choi

et al. 2023

Int. J. Emerg. Technol. Learn.

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The recent progress of natural language processing (NLP), speech recognition, and speech generation envisions using hands-free artificial intelligence (AI) speakers in classrooms to support student learning. In science education, the conventional hands-on laboratory education has been considered crucial in fostering students’ manipulative experimentation skills. However, touching things with gloved hands other than experimental equipment and apparatuses is strictly restricted because of the safety issue, which calls for another channel to get timely support. Therefore, we ideated that adopting hands-free AI speakers in the hands-on science laboratory classroom would support student learning. Using the rapid prototyping method, we designed and developed an AI speaker-based system that answers student queries concerning solution-making, experimental process, and waste liquid disposal, which corresponds to the initial, middle, and final phases of a laboratory class. The system was internally validated by usability tests of 9 expert panels and 18 university students, and then revised. The revised system was externally validated in an analytical chemistry experiment class for 3 sessions with 13 university students. We present the result of the prototype development, internal and external validations with quantitative and qualitative data. The AI speaker system enabled students to use the auditory learning mode in the laboratory while concentrating on the experimentation with their hands in the external validation.

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“…Technological development and the speed of knowledge production in the present century constitute arguments that validate virtuality as a revolution within education [1]. Training through virtual teaching-learning environments is a necessary tool that today many university institutions put into practice in their development strategies and academic perspective [2]. However, it is a challenge for teachers to use these technological resources and adapt them as part of their pedagogical tools, which allow the development of the competences of university students [3,4].…”

Section: Introductionmentioning

confidence: 99%

Virtualization of Teaching and Learning of Engineering Students and its Impact on Self-Perception of Attitude Acquisition, in the Context of COVID-19

Chamorro-Atalaya

Olivares-Zegarra

Alvarado-Bravo

et al. 2021

Int. J. Emerg. Technol. Learn.

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This present describes the results of the evaluation regarding the self-perception of personal and social attitudes acquired by university students from an engineer-ing faculty at a state university in Peru, in the context of virtual teaching and learning, declared by the health emergency by COVID-19; For which the follow-ing objectives were proposed, to determine the variation or impact that the self-perception of personal and social attitudes experienced, having as reference sce-narios, the academic semester with face-to-face teaching (academic semester 2019B) and then the academic semester developed totally virtual (2020A). An exploratory-descriptive research level was used, with a longitudinal non-experimental design, whose population and sample is made up of 674 and 761 students, in the 2019B and 2020A semesters respectively. The data collection in-struments were validated through Cronbach's Alpha, whose average results per academic semester were 0.960 and 0.958. After the investigation, it was found that there is no negative impact, due to virtual teaching; On the contrary, on aver-age, there was an increase in all levels of satisfaction, increasing the level very satisfied by 52.8% and the level satisfied by 3.25%.

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Connotation Analysis and Paradigm Shift of Teaching Design under Artificial Intelligence Technology

Cited by 32 publications

References 15 publications

Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

Do we still need teachers? Navigating the paradigm shift of the teacher's role in the AI era

Development of the Hands-free AI Speaker System Supporting Hands-on Science Laboratory Class

Virtualization of Teaching and Learning of Engineering Students and its Impact on Self-Perception of Attitude Acquisition, in the Context of COVID-19

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