{"title":"A systematic review of conversational AI in language education: focusing on the collaboration with human teachers","authors":"Hyangeun Ji, Insook Han, Yujung Ko","doi":"10.1080/15391523.2022.2142873","DOIUrl":"https://doi.org/10.1080/15391523.2022.2142873","url":null,"abstract":"Abstract Despite the increasing use of conversational artificial intelligence (AI) in language learning, few studies explored how to develop collaborative partnership between AIs and humans. This systematic review examines empirical evidence of human-computer collaboration from 24 studies conducted in an AI-integrated language learning environment and published between 2015 and 2021. The roles of conversational AIs and teachers in each language learning phase with challenges of and suggestions for conversational AI-integrated language learning were identified. Although limited evidence for collaboration between conversational AIs and human teachers was found, future language education should integrate conversational AIs to promote intelligence amplification and decrease human teachers’ workload through classroom orchestration. The study concludes with guidelines and recommendations for teachers and AI researchers.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42512262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Affinity and anonymity benefitting early career teachers in the r/teachers subreddit","authors":"Hunhui Na, K. B. Staudt Willet","doi":"10.1080/15391523.2022.2150727","DOIUrl":"https://doi.org/10.1080/15391523.2022.2150727","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42455844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"What would the matrix do?: a systematic review of K-12 AI learning contexts and learner-interface interactions","authors":"R. Moore, Shiyan Jiang, Brian Abramowitz","doi":"10.1080/15391523.2022.2148785","DOIUrl":"https://doi.org/10.1080/15391523.2022.2148785","url":null,"abstract":"Abstract This systematic review examines the empirical literature published between 2014 and 2021 that situates artificial intelligence within K-12 educational contexts. Our review synthesizes 12 articles and highlights artificial intelligence’s instructional contexts and applications in K-12 learning environments. We focused our synthesis on the learning contexts and the learner-interface interactions. Our findings highlight that most of intelligent systems are being deployed in math or informal settings. Also, there are opportunities for more collaboration to facilitate teaching and learning in domain-specific areas. Additionally, researchers can explore how to implement more collaborative learning opportunities between intelligent tutors and learners. We conclude with a discussion of the reciprocal nature of this technology integration.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45821384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Distributed interpretation – teaching reconstructive methods in the social sciences supported by artificial intelligence","authors":"B. Schäffer, Fabio Roman Lieder","doi":"10.1080/15391523.2022.2148786","DOIUrl":"https://doi.org/10.1080/15391523.2022.2148786","url":null,"abstract":"Abstract This article highlights teaching and learning in reconstructive research supported by artificial intelligence (AI) and machine interpretation in particular. The focus is whether the traditional teaching of methodological competence through research workshops can be supplemented with artificial intelligence (natural language processing, NLP) implemented in computer-assisted qualitative data analysis software (CAQDAS). A case study shows that AI models can be trained to interpret texts. Thus, distributed interpretation by humans and AI becomes possible, opening up new possibilities for teaching qualitative methods. How people deal with these new possibilities is presented based on an explorative evaluation of a group discussion with young researchers. Finally, this contribution discusses the possibilities and limits of this new form of interpretation together with a machine.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49360410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christian W. F. Mayer, Sabrina Ludwig, Steffen Brandt
{"title":"Prompt text classifications with transformer models! An exemplary introduction to prompt-based learning with large language models","authors":"Christian W. F. Mayer, Sabrina Ludwig, Steffen Brandt","doi":"10.1080/15391523.2022.2142872","DOIUrl":"https://doi.org/10.1080/15391523.2022.2142872","url":null,"abstract":"Abstract This study investigates the potential of automated classification using prompt-based learning approaches with transformer models (large language models trained in an unsupervised manner) for a domain-specific classification task. Prompt-based learning with zero or few shots has the potential to (1) make use of artificial intelligence without sophisticated programming skills and (2) make use of artificial intelligence without fine-tuning models with large amounts of labeled training data. We apply this novel method to perform an experiment using so-called zero-shot classification as a baseline model and a few-shot approach for classification. For comparison, we also fine-tune a language model on the given classification task and conducted a second independent human rating to compare it with the given human ratings from the original study. The used dataset consists of 2,088 email responses to a domain-specific problem-solving task that were manually labeled for their professional communication style. With the novel prompt-based learning approach, we achieved a Cohen’s kappa of .40, while the fine-tuning approach yields a kappa of .59, and the new human rating achieved a kappa of .58 with the original human ratings. However, the classifications from the machine learning models have the advantage that each prediction is provided with a reliability estimate allowing us to identify responses that are difficult to score. We, therefore, argue that response ratings should be based on a reciprocal workflow of machine raters and human raters, where the machine rates easy-to-classify responses and the human raters focus and agree on the responses that are difficult to classify. Further, we believe that this new, more intuitive, prompt-based learning approach will enable more people to use artificial intelligence.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41251892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Engaging students via synchronous peer feedback in a technology-enhanced learning environment","authors":"Li-juan Cheng, John Hampton, Swapna Kumar","doi":"10.1080/15391523.2022.2142874","DOIUrl":"https://doi.org/10.1080/15391523.2022.2142874","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45029966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Topics, author profiles, and collaboration networks in the Journal of Research on Technology in Education: A bibliometric analysis of 20 years of research","authors":"Matthew L. Wilson","doi":"10.1080/15391523.2022.2134236","DOIUrl":"https://doi.org/10.1080/15391523.2022.2134236","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43609555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Selena Steinberg, M. Gresalfi, Lauren Vogelstein, C. Brady
{"title":"Coding choreography: Understanding student responses to representational incompatibilities between dance and programming","authors":"Selena Steinberg, M. Gresalfi, Lauren Vogelstein, C. Brady","doi":"10.1080/15391523.2022.2135144","DOIUrl":"https://doi.org/10.1080/15391523.2022.2135144","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41771079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A systematic review of research on technological, pedagogical, and content knowledge (TPACK) for online teaching in the humanities","authors":"Shuqiong Luo, Di Zou","doi":"10.1080/15391523.2022.2139026","DOIUrl":"https://doi.org/10.1080/15391523.2022.2139026","url":null,"abstract":"","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45918135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Literature review of the reciprocal value of artificial and human intelligence in early childhood education","authors":"Lucrezia Crescenzi-Lanna","doi":"10.1080/15391523.2022.2128480","DOIUrl":"https://doi.org/10.1080/15391523.2022.2128480","url":null,"abstract":"Abstract This paper presents a systematic literature review of artificial intelligence (AI)-supported teaching and learning in early childhood. The focus is on human–machine cooperation in education. International evidence and associated problems with the reciprocal contributions of humans and machines are presented and discussed, as well as future horizons regarding AI research in early education. Also, the ethical implications of applying machine learning, deep learning and learning analytics in early childhood education are considered. The method adopted has five steps: identification of the research, evaluation and selection of the literature, data extraction, synthesis, and results. The results shown that AI applications still present limitations in terms of the challenges encountered in early childhood education and data privacy and protection policies.","PeriodicalId":47444,"journal":{"name":"Journal of Research on Technology in Education","volume":null,"pages":null},"PeriodicalIF":5.1,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41944268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}