TY - BOOK TI - Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. AU - European Commission. Directorate General for Education, Youth, Sport and Culture. CY - LU DA - 2022/// PY - 2022 DP - DOI.org (CSL JSON) LA - eng PB - Publications Office UR - https://data.europa.eu/doi/10.2766/153756 Y2 - 2023/04/30/17:18:00 ER - TY - JOUR TI - White paper: Adaptive learning systems AU - Oxman, Steven AU - Wong, William AU - Innovations, D T2 - Integrated Education Solutions DA - 2014/// PY - 2014 SP - 6 EP - 7 J2 - Integrated Education Solutions ER - TY - RPRT TI - GPT-4 AU - OpenAI DA - 2024/03// PY - 2024 UR - https://openai.com/index/gpt-4-research/ ER - TY - JOUR TI - Embracing ChatGPT in the Evolving Landscape of Mathematics Teacher Education and Assessment AU - Hodge-zickerman, A AU - York, Cindy T2 - Exploring New Horizons: Generative Artificial Intelligence and Teacher Education DA - 2024/// PY - 2024 SP - 111 J2 - Exploring New Horizons: Generative Artificial Intelligence and Teacher Education ER - TY - JOUR TI - Who on Earth Is Using Generative AI? AU - Liu, Yan AU - Wang, He DA - 2024/// PY - 2024 ER - TY - ELEC TI - What is Generative AI? AU - Stryker, Cole AU - Scapicchio, Mark T2 - https://www.ibm.com DA - 2024/03/22/ PY - 2024 LA - English ST - What is Generative AI? UR - https://www.ibm.com/topics/generative-ai ER - TY - JOUR TI - Generative AI and teachers’ perspectives on its implementation in education AU - Kaplan-Rakowski, Regina AU - Grotewold, Kimberly AU - Hartwick, Peggy AU - Papin, Kevin T2 - Journal of Interactive Learning Research DA - 2023/// PY - 2023 VL - 34 IS - 2 SP - 313 EP - 338 J2 - Journal of Interactive Learning Research SN - 1093-023X ER - TY - BOOK TI - Should robots replace teachers?: AI and the future of education AU - Selwyn, Neil DA - 2019/// PY - 2019 PB - John Wiley & Sons SN - 1-5095-2898-9 ER - TY - JOUR TI - Chatbots applications in education: A systematic review AU - Okonkwo, Chinedu Wilfred AU - Ade-Ibijola, Abejide T2 - Computers and Education: Artificial Intelligence DA - 2021/// PY - 2021 VL - 2 SP - 100033 J2 - Computers and Education: Artificial Intelligence SN - 2666-920X ER - TY - JOUR TI - Computers in schools in the USA: A social history AU - Parker, Kevin R AU - Davey, Bill T2 - Reflections on the History of Computers in Education: Early use of Computers and Teaching about Computing in Schools DA - 2014/// PY - 2014 SP - 203 EP - 211 J2 - Reflections on the History of Computers in Education: Early use of Computers and Teaching about Computing in Schools SN - 3642551181 ER - TY - JOUR TI - A comprehensive review on generative ai for education AU - Mittal, Uday AU - Sai, Siva AU - Chamola, Vinay T2 - IEEE Access DA - 2024/// PY - 2024 J2 - IEEE Access SN - 2169-3536 ER - TY - JOUR TI - Teachers’ readiness and intention to teach artificial intelligence in schools AU - Ayanwale, Musa Adekunle AU - Sanusi, Ismaila Temitayo AU - Adelana, Owolabi Paul AU - Aruleba, Kehinde D. AU - Oyelere, Solomon Sunday T2 - Computers and Education: Artificial Intelligence DA - 2022/// PY - 2022 DO - 10.1016/j.caeai.2022.100099 DP - DOI.org (Crossref) VL - 3 SP - 100099 J2 - Computers and Education: Artificial Intelligence LA - en SN - 2666920X UR - https://linkinghub.elsevier.com/retrieve/pii/S2666920X22000546 Y2 - 2025/04/29/07:29:10 L1 - files/5146/Ayanwale et al. - 2022 - Teachers’ readiness and intention to teach artificial intelligence in schools.pdf ER - TY - JOUR TI - OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare AU - Aydın, Ömer AU - Karaarslan, Enis T2 - SSRN Electronic Journal DA - 2022/// PY - 2022 DO - 10.2139/ssrn.4308687 DP - DOI.org (Crossref) J2 - SSRN Journal LA - en SN - 1556-5068 ST - OpenAI ChatGPT Generated Literature Review UR - https://www.ssrn.com/abstract=4308687 Y2 - 2025/04/29/07:29:26 ER - TY - JOUR TI - Self-efficacy: The exercise of control AU - Bandura, Albert DA - 1997/// PY - 1997 ER - TY - RPRT TI - The Rapid Adoption of Generative AI AU - Bick, Alexander AU - Blandin, Adam AU - Deming, David CY - Cambridge, MA DA - 2024/09// PY - 2024 DP - DOI.org (Crossref) SP - w32966 LA - en PB - National Bureau of Economic Research SN - w32966 UR - http://www.nber.org/papers/w32966.pdf Y2 - 2025/04/29/07:30:16 ER - TY - CONF TI - Fairness in machine learning: Lessons from political philosophy AU - Binns, Reuben T2 - Conference on fairness, accountability and transparency DA - 2018/// PY - 2018 SP - 149 EP - 159 PB - PMLR SN - 2640-3498 ER - TY - GEN TI - On the Opportunities and Risks of Foundation Models AU - Bommasani, Rishi AU - Hudson, Drew A. AU - Adeli, Ehsan AU - Altman, Russ AU - Arora, Simran AU - von Arx, Sydney AU - Bernstein, Michael S. AU - Bohg, Jeannette AU - Bosselut, Antoine AU - Brunskill, Emma AU - Brynjolfsson, Erik AU - Buch, Shyamal AU - Card, Dallas AU - Castellon, Rodrigo AU - Chatterji, Niladri AU - Chen, Annie AU - Creel, Kathleen AU - Davis, Jared Quincy AU - Demszky, Dora AU - Donahue, Chris AU - Doumbouya, Moussa AU - Durmus, Esin AU - Ermon, Stefano AU - Etchemendy, John AU - Ethayarajh, Kawin AU - Fei-Fei, Li AU - Finn, Chelsea AU - Gale, Trevor AU - Gillespie, Lauren AU - Goel, Karan AU - Goodman, Noah AU - Grossman, Shelby AU - Guha, Neel AU - Hashimoto, Tatsunori AU - Henderson, Peter AU - Hewitt, John AU - Ho, Daniel E. AU - Hong, Jenny AU - Hsu, Kyle AU - Huang, Jing AU - Icard, Thomas AU - Jain, Saahil AU - Jurafsky, Dan AU - Kalluri, Pratyusha AU - Karamcheti, Siddharth AU - Keeling, Geoff AU - Khani, Fereshte AU - Khattab, Omar AU - Koh, Pang Wei AU - Krass, Mark AU - Krishna, Ranjay AU - Kuditipudi, Rohith AU - Kumar, Ananya AU - Ladhak, Faisal AU - Lee, Mina AU - Lee, Tony AU - Leskovec, Jure AU - Levent, Isabelle AU - Li, Xiang Lisa AU - Li, Xuechen AU - Ma, Tengyu AU - Malik, Ali AU - Manning, Christopher D. AU - Mirchandani, Suvir AU - Mitchell, Eric AU - Munyikwa, Zanele AU - Nair, Suraj AU - Narayan, Avanika AU - Narayanan, Deepak AU - Newman, Ben AU - Nie, Allen AU - Niebles, Juan Carlos AU - Nilforoshan, Hamed AU - Nyarko, Julian AU - Ogut, Giray AU - Orr, Laurel AU - Papadimitriou, Isabel AU - Park, Joon Sung AU - Piech, Chris AU - Portelance, Eva AU - Potts, Christopher AU - Raghunathan, Aditi AU - Reich, Rob AU - Ren, Hongyu AU - Rong, Frieda AU - Roohani, Yusuf AU - Ruiz, Camilo AU - Ryan, Jack AU - Ré, Christopher AU - Sadigh, Dorsa AU - Sagawa, Shiori AU - Santhanam, Keshav AU - Shih, Andy AU - Srinivasan, Krishnan AU - Tamkin, Alex AU - Taori, Rohan AU - Thomas, Armin W. AU - Tramèr, Florian AU - Wang, Rose E. AU - Wang, William AU - Wu, Bohan AU - Wu, Jiajun AU - Wu, Yuhuai AU - Xie, Sang Michael AU - Yasunaga, Michihiro AU - You, Jiaxuan AU - Zaharia, Matei AU - Zhang, Michael AU - Zhang, Tianyi AU - Zhang, Xikun AU - Zhang, Yuhui AU - Zheng, Lucia AU - Zhou, Kaitlyn AU - Liang, Percy AB - AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature. DA - 2021/// PY - 2021 DO - 10.48550/ARXIV.2108.07258 DP - DOI.org (Datacite) PB - arXiv UR - https://arxiv.org/abs/2108.07258 Y2 - 2025/04/29/07:30:56 KW - Artificial Intelligence (cs.AI) KW - Computers and Society (cs.CY) KW - FOS: Computer and information sciences KW - Machine Learning (cs.LG) ER - TY - JOUR TI - Geometric Loci and ChatGPT: Caveat Emptor! AU - Botana, Francisco AU - Recio, Tomas T2 - Computation AB - We compare the performance of two systems, ChatGPT 3.5 and GeoGebra 5, in a restricted, but quite relevant, benchmark from the realm of classical geometry: the determination of geometric loci, focusing, in particular, on the computation of envelopes of families of plane curves. In order to study the loci calculation abilities of ChatGPT, we begin by entering an informal description of a geometric construction involving a locus or an envelope and then we ask ChatGPT to compute its equation. The chatbot fails in most situations, showing that it is not mature enough to deal with the subject. Then, the same constructions are also approached through the automated reasoning tools implemented in the dynamic geometry program, GeoGebra Discovery, which successfully resolves most of them. Furthermore, although ChatGPT is able to write general computer code, it cannot currently output that of GeoGebra. Thus, we consider describing a simple method for ChatGPT to generate GeoGebra constructions. Finally, in case GeoGebra fails, or gives an incorrect solution, we refer to the need for improved computer algebra algorithms to solve the loci/envelope constructions. Other than exhibiting the current problematic performance of the involved programs in this geometric context, our comparison aims to show the relevance and benefits of analyzing the interaction between them. DA - 2024/02/07/ PY - 2024 DO - 10.3390/computation12020030 DP - DOI.org (Crossref) VL - 12 IS - 2 SP - 30 J2 - Computation LA - en SN - 2079-3197 ST - Geometric Loci and ChatGPT UR - https://www.mdpi.com/2079-3197/12/2/30 Y2 - 2025/04/29/07:31:07 L1 - files/5154/Botana and Recio - 2024 - Geometric Loci and ChatGPT Caveat Emptor!.pdf ER - TY - JOUR TI - Back-Translation for Cross-Cultural Research AU - Brislin, Richard W. T2 - Journal of Cross-Cultural Psychology AB - Two aspects of translation were investigated: (1) factors that affect translation quality, and (2) how equivalence between source and target versions can be evaluated. The variables of language, content, and difficulty were studied through an analysis of variance design. Ninety-four bilinguals from the University of Guam, representing ten languages, translated or back-translated six essays incorporating three content areas and two levels of difficulty. The five criteria for equivalence were based on comparisons of meaning or predictions of similar responses to original or translated versions. The factors of content, difficulty, language and content-language interaction were significant, and the five equivalence criteria proved workable. Conclusions are that translation quality can be predicted, and that a functionally equivalent translation can be demonstrated when responses to the original and target versions are studied. DA - 1970/09// PY - 1970 DO - 10.1177/135910457000100301 DP - DOI.org (Crossref) VL - 1 IS - 3 SP - 185 EP - 216 J2 - Journal of Cross-Cultural Psychology LA - en SN - 0022-0221, 1552-5422 UR - https://journals.sagepub.com/doi/10.1177/135910457000100301 Y2 - 2025/04/29/07:31:15 ER - TY - CONF TI - What Does it Mean for a Language Model to Preserve Privacy? AU - Brown, Hannah AU - Lee, Katherine AU - Mireshghallah, Fatemehsadat AU - Shokri, Reza AU - Tramèr, Florian T2 - FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency C1 - Seoul Republic of Korea C3 - 2022 ACM Conference on Fairness, Accountability, and Transparency DA - 2022/06/21/ PY - 2022 DO - 10.1145/3531146.3534642 DP - DOI.org (Crossref) SP - 2280 EP - 2292 LA - en PB - ACM SN - 978-1-4503-9352-2 UR - https://dl.acm.org/doi/10.1145/3531146.3534642 Y2 - 2025/04/29/07:31:25 L1 - files/5157/Brown et al. - 2022 - What Does it Mean for a Language Model to Preserve Privacy.pdf ER - TY - GEN TI - Affordances, Challenges, and Opportunities of ChatGPT in Mathematics Education: A Scoping Review AU - Bui, Phuong AU - Pongsakdi, Nonmanut AU - McMullen, Jake AU - Veermans, Marjaana AB - The rapid rise of ChatGPT and other large language models (LLMs) has drawn significant attention to their potential impacts on research and educational practices, necessitating critical evaluation and adaptation. In mathematics education, questions remain about the affordances, challenges and opportunities associated with these Generative Artificial Intelligence (Gen AI) tools. This study presents a comprehensive scoping review of 28 studies to examine the current applications, methodologies, and impacts of ChatGPT in mathematics education. Our findings indicate that most research emphasizes tool-driven evaluations of ChatGPT’s performance in solving mathematical problems, with limited exploration of human-AI interactions and user perceptions. While studies span various educational levels, the majority addresses K-12 mathematics. ChatGPT shows strengths in solving procedural and straightforward problems, providing step-by-step explanations, and clarifying concepts especially in lower-complexity mathematics. However, its limitations in solving complex, multistep problems, or those that require visual and spatial reasoning, and its tendencies to hallucinate or produce verbose responses pose challenges, particularly for learners with limited mathematical proficiency. These findings underscore the need for future research on ChatGPT’s integration in specific educational contexts, with a focus on human-driven approaches and development of pedagogical practices. They also highlight the potential of ChatGPT and similar Gen AI tools in mathematics education when used thoughtfully and strategically. To realize this potential, teacher training and professional development should focus on equipping educators with technological knowledge and technical skills necessary to effectively incorporate Gen AI tools into their instructional practices. DA - 2024/12/05/ PY - 2024 DO - 10.31219/osf.io/ce2ky DP - Open Science Framework ST - Affordances, Challenges, and Opportunities of ChatGPT in Mathematics Education UR - https://osf.io/ce2ky Y2 - 2025/04/29/07:31:42 ER - TY - JOUR TI - Using the technology acceptance model to assess how preservice teachers’ view educational technology in middle and high school classrooms AU - Casey, J. Elizabeth AU - Kirk, Jeff AU - Kuklies, Kimberly AU - Mireles, Selina V. T2 - Education and Information Technologies DA - 2023/02// PY - 2023 DO - 10.1007/s10639-022-11263-6 DP - DOI.org (Crossref) VL - 28 IS - 2 SP - 2361 EP - 2382 J2 - Educ Inf Technol LA - en SN - 1360-2357, 1573-7608 UR - https://link.springer.com/10.1007/s10639-022-11263-6 Y2 - 2025/04/29/07:31:50 ER - TY - JOUR TI - Technology Acceptance Model: Assessing Preservice Teachers’ Acceptance of Floor-Robots as a Useful Pedagogical Tool AU - Casey, J. Elizabeth AU - Pennington, Lisa K. AU - Mireles, Selina V. T2 - Technology, Knowledge and Learning DA - 2021/09// PY - 2021 DO - 10.1007/s10758-020-09452-8 DP - DOI.org (Crossref) VL - 26 IS - 3 SP - 499 EP - 514 J2 - Tech Know Learn LA - en SN - 2211-1662, 2211-1670 ST - Technology Acceptance Model UR - https://link.springer.com/10.1007/s10758-020-09452-8 Y2 - 2025/04/29/07:32:00 ER - TY - JOUR TI - The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research AU - Celik, Ismail AU - Dindar, Muhterem AU - Muukkonen, Hanni AU - Järvelä, Sanna T2 - TechTrends AB - Abstract This study provides an overview of research on teachers’ use of artificial intelligence (AI) applications and machine learning methods to analyze teachers’ data. Our analysis showed that AI offers teachers several opportunities for improved planning (e.g., by defining students’ needs and familiarizing teachers with such needs), implementation (e.g., through immediate feedback and teacher intervention), and assessment (e.g., through automated essay scoring) of their teaching. We also found that teachers have various roles in the development of AI technology. These roles include acting as models for training AI algorithms and participating in AI development by checking the accuracy of AI automated assessment systems. Our findings further underlined several challenges in AI implementation in teaching practice, which provide guidelines for developing the field. DA - 2022/07// PY - 2022 DO - 10.1007/s11528-022-00715-y DP - DOI.org (Crossref) VL - 66 IS - 4 SP - 616 EP - 630 J2 - TechTrends LA - en SN - 8756-3894, 1559-7075 ST - The Promises and Challenges of Artificial Intelligence for Teachers UR - https://link.springer.com/10.1007/s11528-022-00715-y Y2 - 2025/04/29/07:32:53 L1 - files/5162/Celik et al. - 2022 - The Promises and Challenges of Artificial Intelligence for Teachers a Systematic Review of Research.pdf ER - TY - JOUR TI - Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age AU - Dai, Yun AU - Chai, Ching-Sing AU - Lin, Pei-Yi AU - Jong, Morris Siu-Yung AU - Guo, Yanmei AU - Qin, Jianjun T2 - Sustainability AB - This study developed and validated an instrument to measure students’ readiness to learn about artificial intelligence (AI). The designed survey questionnaire was administrated in a school district in Beijing after an AI course was developed and implemented. The collected data and analytical results provided insights regarding the self-reported perceptions of primary students’ AI readiness and enabled the identification of factors that may influence this parameter. The results indicated that AI literacy was not predictive of AI readiness. The influences of AI literacy were mediated by the students’ confidence and perception of AI relevance. The students’ AI readiness was not influenced by a reduction in their anxiety regarding AI and an enhancement in their AI literacy. Male students reported a higher confidence, relevance, and readiness for AI than female students did. The sentiments reflected by the open-ended responses of the students indicated that the students were generally excited to learn about AI and viewed AI as a powerful and useful technology. The student sentiments confirmed the quantitative findings. The validated survey can help teachers better understand and monitor students’ learning, as well as reflect on the design of the AI curriculum and the associated teaching effectiveness. DA - 2020/08/14/ PY - 2020 DO - 10.3390/su12166597 DP - DOI.org (Crossref) VL - 12 IS - 16 SP - 6597 J2 - Sustainability LA - en SN - 2071-1050 UR - https://www.mdpi.com/2071-1050/12/16/6597 Y2 - 2025/04/29/07:33:03 L1 - files/5164/Dai et al. - 2020 - Promoting Students’ Well-Being by Developing Their Readiness for the Artificial Intelligence Age.pdf ER - TY - JOUR TI - Factors determining behavioral intentions to use Islamic financial technology: Three competing models AU - Darmansyah, Darmansyah AU - Fianto, Bayu Arie AU - Hendratmi, Achsania AU - Aziz, Primandanu Febriyan T2 - Journal of Islamic Marketing AB - Purpose The purpose of this paper is to investigate the influential factors on behavioral intentions toward Islamic financial technology (FinTech) use in Indonesia, for all types of FinTech services as follows: payments, peer to peer lending and crowdfunding. Design/methodology/approach This study adopted structural equation modeling using the partial least squares approach to test the hypotheses. Based on purposive sampling, the questionnaire was distributed through an online survey and received 1,262 responses. Findings The results demonstrate that the latent variables, planned behavior, acceptance model and use of technology, have a significant impact on encouraging behavioral intentions to use Islamic FinTech. The “acceptance model” latent variable is the most influential factor. Research limitations/implications This study was conducted only in Indonesia; therefore, the results cannot be generalized to other countries. However, the study provides important strategic guidelines for policymakers in designing a framework to enhance the development of Islamic FinTech and to achieve financial inclusion. It is suggested that future studies include samples from FinTech users in different countries. Originality/value This study adds to the literature especially on the factors affecting behavioral intentions to use Islamic FinTech. There are limited studies concerning this topic, especially for Indonesia. The unique feature of this study is the use of a large primary data set that covers most provinces in Indonesia. Furthermore, this study focuses on three types of Islamic FinTech, namely, payments, peer to peer lending and crowdfunding. DA - 2021/05/12/ PY - 2021 DO - 10.1108/JIMA-12-2019-0252 DP - DOI.org (Crossref) VL - 12 IS - 4 SP - 794 EP - 812 J2 - JIMA LA - en SN - 1759-0833, 1759-0833 ST - Factors determining behavioral intentions to use Islamic financial technology UR - https://www.emerald.com/insight/content/doi/10.1108/JIMA-12-2019-0252/full/html Y2 - 2025/04/29/07:33:12 ER - TY - JOUR TI - Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology AU - Davis, Fred D. T2 - MIS Quarterly DA - 1989/09// PY - 1989 DO - 10.2307/249008 DP - DOI.org (Crossref) VL - 13 IS - 3 SP - 319 J2 - MIS Quarterly SN - 02767783 UR - https://www.jstor.org/stable/249008?origin=crossref Y2 - 2025/04/29/07:33:20 ER - TY - JOUR TI - Optimizing human-AI collaboration: Effects of motivation and accuracy information in AI-supported decision-making AU - Eisbach, Simon AU - Langer, Markus AU - Hertel, Guido T2 - Computers in Human Behavior: Artificial Humans DA - 2023/08// PY - 2023 DO - 10.1016/j.chbah.2023.100015 DP - DOI.org (Crossref) VL - 1 IS - 2 SP - 100015 J2 - Computers in Human Behavior: Artificial Humans LA - en SN - 29498821 ST - Optimizing human-AI collaboration UR - https://linkinghub.elsevier.com/retrieve/pii/S2949882123000154 Y2 - 2025/04/29/07:33:29 ER - TY - JOUR TI - Technology integration of pre-service teachers explained by attitudes and beliefs, competency, access, and experience AU - Farjon, Daan AU - Smits, Anneke AU - Voogt, Joke T2 - Computers & Education DA - 2019/03// PY - 2019 DO - 10.1016/j.compedu.2018.11.010 DP - DOI.org (Crossref) VL - 130 SP - 81 EP - 93 J2 - Computers & Education LA - en SN - 03601315 UR - https://linkinghub.elsevier.com/retrieve/pii/S0360131518303130 Y2 - 2025/04/29/07:34:08 ER - TY - JOUR TI - Pre-service teachers and ChatGPT in multistrategy problem-solving: Implications for mathematics teaching in primary schools AU - Getenet, Seyum T2 - International Electronic Journal of Mathematics Education AB - This study compared the problem-solving abilities of ChatGPT and 58 pre-service teachers (PSTs) in solving a mathematical word problem using various strategies. PSTs were asked to solve a problem individually. Data was collected from PSTs’ submitted assignments, and their problem-solving strategies were analyzed. ChatGPT was also given the same problem to solve with various prompts, and the correctness of its solutions and problem-solving strategies were assessed alongside those of PSTs. The results indicated that PSTs used diverse strategies and achieved accurate solutions, but not always relevant strategies to children’s level of understanding. ChatGPT employed similar strategies to PSTs but mostly produced incorrect solutions, and its performance needed to be contextualized in the primary school context. The study highlights the potential of ChatGPT in mathematics teaching and informs teacher education programs about the possibility of using it in teaching problem-solving strategies. DA - 2024/01/23/ PY - 2024 DO - 10.29333/iejme/14141 DP - DOI.org (Crossref) VL - 19 IS - 1 SP - em0766 J2 - INT ELECT J MATH ED SN - 1306-3030 ST - Pre-service teachers and ChatGPT in multistrategy problem-solving UR - https://www.iejme.com/article/pre-service-teachers-and-chatgpt-in-multistrategy-problem-solving-implications-for-mathematics-14141 Y2 - 2025/04/29/07:34:18 L1 - files/5170/Getenet - 2024 - Pre-service teachers and ChatGPT in multistrategy problem-solving Implications for mathematics teac.pdf ER - TY - JOUR TI - Using ChatGPT to conduct a literature review AU - Haman, Michael AU - Školník, Milan T2 - Accountability in Research DA - 2024/12/06/ PY - 2024 DO - 10.1080/08989621.2023.2185514 DP - DOI.org (Crossref) VL - 31 IS - 8 SP - 1244 EP - 1246 J2 - Accountability in Research LA - en SN - 0898-9621, 1545-5815 UR - https://www.tandfonline.com/doi/full/10.1080/08989621.2023.2185514 Y2 - 2025/04/29/07:34:31 ER - TY - JOUR TI - Measuring perceived sociability of computer-supported collaborative learning environments AU - Kreijns, Karel AU - Kirschner, Paul A. AU - Jochems, Wim AU - Van Buuren, Hans T2 - Computers & Education DA - 2007/09// PY - 2007 DO - 10.1016/j.compedu.2005.05.004 DP - DOI.org (Crossref) VL - 49 IS - 2 SP - 176 EP - 192 J2 - Computers & Education LA - en SN - 03601315 UR - https://linkinghub.elsevier.com/retrieve/pii/S0360131505000904 Y2 - 2025/04/29/07:35:40 L1 - files/5173/Kreijns et al. - 2007 - Measuring perceived sociability of computer-supported collaborative learning environments.pdf ER - TY - JOUR TI - Fine-tuning ChatGPT for automatic scoring AU - Latif, Ehsan AU - Zhai, Xiaoming T2 - Computers and Education: Artificial Intelligence DA - 2024/06// PY - 2024 DO - 10.1016/j.caeai.2024.100210 DP - DOI.org (Crossref) VL - 6 SP - 100210 J2 - Computers and Education: Artificial Intelligence LA - en SN - 2666920X UR - https://linkinghub.elsevier.com/retrieve/pii/S2666920X24000110 Y2 - 2025/04/29/07:35:53 L1 - files/5175/Latif and Zhai - 2024 - Fine-tuning ChatGPT for automatic scoring.pdf ER - TY - JOUR TI - Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review AU - Law, Locky T2 - Computers and Education Open DA - 2024/06// PY - 2024 DO - 10.1016/j.caeo.2024.100174 DP - DOI.org (Crossref) VL - 6 SP - 100174 J2 - Computers and Education Open LA - en SN - 26665573 ST - Application of generative artificial intelligence (GenAI) in language teaching and learning UR - https://linkinghub.elsevier.com/retrieve/pii/S2666557324000156 Y2 - 2025/04/29/07:36:01 ER - TY - JOUR TI - Trust in Automation: Designing for Appropriate Reliance AU - Lee, J. D. AU - See, K. A. T2 - Human Factors: The Journal of the Human Factors and Ergonomics Society DA - 2004/01/01/ PY - 2004 DO - 10.1518/hfes.46.1.50_30392 DP - DOI.org (Crossref) VL - 46 IS - 1 SP - 50 EP - 80 J2 - Human Factors: The Journal of the Human Factors and Ergonomics Society LA - en SN - 0018-7208 ST - Trust in Automation UR - http://hfs.sagepub.com/cgi/doi/10.1518/hfes.46.1.50_30392 Y2 - 2025/04/29/07:36:08 ER - TY - JOUR TI - Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory AU - Li, Jian AU - Huang, Jin-Song T2 - Technology in Society DA - 2020/11// PY - 2020 DO - 10.1016/j.techsoc.2020.101410 DP - DOI.org (Crossref) VL - 63 SP - 101410 J2 - Technology in Society LA - en SN - 0160791X UR - https://linkinghub.elsevier.com/retrieve/pii/S0160791X20300476 Y2 - 2025/04/29/07:36:16 ER - TY - CHAP TI - Solving the Self-regulated Learning Problem: Exploring the Performance of ChatGPT in Mathematics AU - Li, Pin-Hui AU - Lee, Hsin-Yu AU - Cheng, Yu-Ping AU - Starčič, Andreja Istenič AU - Huang, Yueh-Min T2 - Innovative Technologies and Learning A2 - Huang, Yueh-Min A2 - Rocha, Tânia CY - Cham DA - 2023/// PY - 2023 DP - DOI.org (Crossref) VL - 14099 SP - 77 EP - 86 LA - en PB - Springer Nature Switzerland SN - 978-3-031-40112-1 978-3-031-40113-8 ST - Solving the Self-regulated Learning Problem UR - https://link.springer.com/10.1007/978-3-031-40113-8_8 Y2 - 2025/04/29/07:36:23 ER - TY - JOUR TI - Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators AU - Lim, Weng Marc AU - Gunasekara, Asanka AU - Pallant, Jessica Leigh AU - Pallant, Jason Ian AU - Pechenkina, Ekaterina T2 - The International Journal of Management Education DA - 2023/07// PY - 2023 DO - 10.1016/j.ijme.2023.100790 DP - DOI.org (Crossref) VL - 21 IS - 2 SP - 100790 J2 - The International Journal of Management Education LA - en SN - 14728117 ST - Generative AI and the future of education UR - https://linkinghub.elsevier.com/retrieve/pii/S1472811723000289 Y2 - 2025/04/29/07:36:33 ER - TY - JOUR TI - The influence of ChatGPT on student engagement: A systematic review and future research agenda AU - Lo, Chung Kwan AU - Hew, Khe Foon AU - Jong, Morris Siu-yung T2 - Computers & Education DA - 2024/10// PY - 2024 DO - 10.1016/j.compedu.2024.105100 DP - DOI.org (Crossref) VL - 219 SP - 105100 J2 - Computers & Education LA - en SN - 03601315 ST - The influence of ChatGPT on student engagement UR - https://linkinghub.elsevier.com/retrieve/pii/S0360131524001143 Y2 - 2025/04/29/07:36:52 ER - TY - JOUR TI - It’s not like a calculator, so what is the relationship between learners and generative artificial intelligence? AU - Lodge, Jason M. AU - Yang, Suijing AU - Furze, Leon AU - Dawson, Phillip T2 - Learning: Research and Practice DA - 2023/07/03/ PY - 2023 DO - 10.1080/23735082.2023.2261106 DP - DOI.org (Crossref) VL - 9 IS - 2 SP - 117 EP - 124 J2 - Learning: Research and Practice LA - en SN - 2373-5082, 2373-5090 UR - https://www.tandfonline.com/doi/full/10.1080/23735082.2023.2261106 Y2 - 2025/04/29/07:37:03 ER - TY - JOUR TI - The use of ChatGPT in teaching and learning: a systematic review through SWOT analysis approach AU - Mai, Duong Thi Thuy AU - Da, Can Van AU - Hanh, Nguyen Van T2 - Frontiers in Education AB - Introduction The integration of ChatGPT, an advanced AI-powered chatbot, into educational settings, has caused mixed reactions among educators. Therefore, we conducted a systematic review to explore the strengths and weaknesses of using ChatGPT and discuss the opportunities and threats of using ChatGPT in teaching and learning. Methods Following the PRISMA flowchart guidelines, 51 articles were selected among 819 studies collected from Scopus, ERIC and Google Scholar databases in the period from 2022-2023. Results The synthesis of data extracted from the 51 included articles revealed 32 topics including 13 strengths, 10 weaknesses, 5 opportunities and 4 threats of using ChatGPT in teaching and learning. We used Biggs’s Presage-Process-Product (3P) model of teaching and learning to categorize topics into three components of the 3P model. Discussion In the Presage stage, we analyzed how ChatGPT interacts with student characteristics and teaching contexts to ensure that the technology adapts effectively to diverse needs and backgrounds. In the Process stage, we analyzed how ChatGPT impacted teaching and learning activities to determine its ability to provide personalized, adaptive, and effective instructional support. Finally, in the Product stage, we evaluated how ChatGPT contributed to student learning outcomes. By carefully considering its application in each stage of teaching and learning, educators can make informed decisions, leveraging the strengths and addressing the weaknesses of ChatGPT to optimize its integration into teaching and learning processes. DA - 2024/02/09/ PY - 2024 DO - 10.3389/feduc.2024.1328769 DP - DOI.org (Crossref) VL - 9 SP - 1328769 J2 - Front. Educ. SN - 2504-284X ST - The use of ChatGPT in teaching and learning UR - https://www.frontiersin.org/articles/10.3389/feduc.2024.1328769/full Y2 - 2025/04/29/07:37:09 L1 - files/5184/Mai et al. - 2024 - The use of ChatGPT in teaching and learning a systematic review through SWOT analysis approach.pdf ER - TY - JOUR TI - ChatGPT: The Good, The Bad, and Everything in Between AU - Manohar, Naveen AU - Prasad, Shruthi S. AU - Pise, Gajanan T2 - Indian Dermatology Online Journal DA - 2024/01// PY - 2024 DO - 10.4103/idoj.idoj_274_23 DP - DOI.org (Crossref) VL - 15 IS - 1 SP - 166 EP - 168 LA - en SN - 2229-5178, 2249-5673 ST - ChatGPT UR - https://journals.lww.com/10.4103/idoj.idoj_274_23 Y2 - 2025/04/29/07:37:18 ER - TY - JOUR TI - Systematic review of research on artificial intelligence in K-12 education (2017–2022) AU - Martin, Florence AU - Zhuang, Min AU - Schaefer, Darlene T2 - Computers and Education: Artificial Intelligence DA - 2024/06// PY - 2024 DO - 10.1016/j.caeai.2023.100195 DP - DOI.org (Crossref) VL - 6 SP - 100195 J2 - Computers and Education: Artificial Intelligence LA - en SN - 2666920X UR - https://linkinghub.elsevier.com/retrieve/pii/S2666920X23000747 Y2 - 2025/04/29/07:37:28 ER - TY - JOUR TI - A Survey on Bias and Fairness in Machine Learning AU - Mehrabi, Ninareh AU - Morstatter, Fred AU - Saxena, Nripsuta AU - Lerman, Kristina AU - Galstyan, Aram T2 - ACM Computing Surveys AB - With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields. DA - 2022/07/31/ PY - 2022 DO - 10.1145/3457607 DP - DOI.org (Crossref) VL - 54 IS - 6 SP - 1 EP - 35 J2 - ACM Comput. Surv. LA - en SN - 0360-0300, 1557-7341 UR - https://dl.acm.org/doi/10.1145/3457607 Y2 - 2025/04/29/07:37:37 L1 - files/5188/Mehrabi et al. - 2022 - A Survey on Bias and Fairness in Machine Learning.pdf ER - TY - JOUR TI - TPACK in the age of ChatGPT and Generative AI AU - Mishra, Punya AU - Warr, Melissa AU - Islam, Rezwana T2 - Journal of Digital Learning in Teacher Education DA - 2023/10/02/ PY - 2023 DO - 10.1080/21532974.2023.2247480 DP - DOI.org (Crossref) VL - 39 IS - 4 SP - 235 EP - 251 J2 - Journal of Digital Learning in Teacher Education LA - en SN - 2153-2974, 2332-7383 UR - https://www.tandfonline.com/doi/full/10.1080/21532974.2023.2247480 Y2 - 2025/04/29/07:37:45 ER - TY - JOUR TI - A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research AU - Naeem, Muhammad AU - Ozuem, Wilson AU - Howell, Kerry AU - Ranfagni, Silvia T2 - International Journal of Qualitative Methods AB - Thematic analysis is a highly popular technique among qualitative researchers for analyzing qualitative data, which usually comprises thick descriptive data. However, the application and use of thematic analysis has also involved complications due to confusion regarding the final outcome’s presentation as a conceptual model. This paper develops a systematic thematic analysis process for creating a conceptual model from qualitative research findings. It explores the adaptability of the proposed process across various research methodologies, including constructivist methodologies, positivist methodologies, grounded theory, and interpretive phenomenology, and justifies their application. The paper distinguishes between inductive and deductive coding approaches and emphasizes the merits of each. It suggests that the derived systematic thematic analysis model is valuable across multiple disciplines, particularly in grounded theory, ethnographic approaches, and narrative approaches, while also being adaptable to more descriptive, positivist-based methodologies. By providing a methodological roadmap, this study enhances the rigor and replicability of thematic analysis and offers a comprehensive strategy for theoretical conceptualization in qualitative research. The contribution of this paper is a systematic six-step thematic analysis process that leads to the development of a conceptual model; each step is described in detail and examples are given. DA - 2023/10// PY - 2023 DO - 10.1177/16094069231205789 DP - DOI.org (Crossref) VL - 22 SP - 16094069231205789 J2 - International Journal of Qualitative Methods LA - en SN - 1609-4069, 1609-4069 UR - https://journals.sagepub.com/doi/10.1177/16094069231205789 Y2 - 2025/04/29/07:38:23 L1 - files/5191/Naeem et al. - 2023 - A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research.pdf ER - TY - CHAP TI - Evaluating ChatGPT’s Decimal Skills and Feedback Generation in a Digital Learning Game AU - Nguyen, Huy A. AU - Stec, Hayden AU - Hou, Xinying AU - Di, Sarah AU - McLaren, Bruce M. T2 - Responsive and Sustainable Educational Futures A2 - Viberg, Olga A2 - Jivet, Ioana A2 - Muñoz-Merino, Pedro J. A2 - Perifanou, Maria A2 - Papathoma, Tina CY - Cham DA - 2023/// PY - 2023 DP - DOI.org (Crossref) VL - 14200 SP - 278 EP - 293 LA - en PB - Springer Nature Switzerland SN - 978-3-031-42681-0 978-3-031-42682-7 UR - https://link.springer.com/10.1007/978-3-031-42682-7_19 Y2 - 2025/04/29/07:38:33 ER - TY - JOUR TI - Teacher value beliefs associated with using technology: Addressing professional and student needs AU - Ottenbreit-Leftwich, Anne T. AU - Glazewski, Krista D. AU - Newby, Timothy J. AU - Ertmer, Peggy A. T2 - Computers & Education DA - 2010/11// PY - 2010 DO - 10.1016/j.compedu.2010.06.002 DP - DOI.org (Crossref) VL - 55 IS - 3 SP - 1321 EP - 1335 J2 - Computers & Education LA - en SN - 03601315 ST - Teacher value beliefs associated with using technology UR - https://linkinghub.elsevier.com/retrieve/pii/S0360131510001612 Y2 - 2025/04/29/07:39:24 ER - TY - JOUR TI - An Updated and Streamlined Technology Readiness Index: TRI 2.0 AU - Parasuraman, A. AU - Colby, Charles L. T2 - Journal of Service Research AB - The Technology Readiness Index (TRI), a 36-item scale to measure people’s propensity to embrace and use cutting-edge technologies, was published in the Journal of Service Research over a decade ago. Researchers have since used it in a variety of contexts in over two dozen countries. Meanwhile, several revolutionary technologies (mobile commerce, social media, and cloud computing) that were in their infancy just a decade ago are now pervasive and significantly impacting people’s lives. Based on insights from extensive experience with the TRI and given the significant changes in the technology landscape, the authors undertook a two-phase research project to update and streamline the TRI. After providing a brief overview of technology readiness and the original TRI, this article (a) describes the multiple research stages and analyses that produced TRI 2.0, a 16-item scale; (b) compares TRI 2.0 with the original TRI in terms of content, structure, and psychometric properties; and (c) demonstrates TRI 2.0’s reliability, validity, and usefulness as a customer segmentation tool. The article concludes with potential applications of TRI 2.0 and directions for future research. DA - 2015/02// PY - 2015 DO - 10.1177/1094670514539730 DP - DOI.org (Crossref) VL - 18 IS - 1 SP - 59 EP - 74 J2 - Journal of Service Research LA - en SN - 1094-6705, 1552-7379 ST - An Updated and Streamlined Technology Readiness Index UR - https://journals.sagepub.com/doi/10.1177/1094670514539730 Y2 - 2025/04/29/07:39:45 ER - TY - JOUR TI - Can Generative AI Solve Geometry Problems? Strengths and Weaknesses of LLMs for Geometric Reasoning in Spanish AU - Parra, Verónica AU - Sureda, Patricia AU - Corica, Ana AU - Schiaffino, Silvia AU - Godoy, Daniela T2 - International Journal of Interactive Multimedia and Artificial Intelligence DA - 2024/// PY - 2024 DO - 10.9781/ijimai.2024.02.009 DP - DOI.org (Crossref) VL - 8 IS - 5 SP - 65 J2 - IJIMAI LA - en SN - 1989-1660 ST - Can Generative AI Solve Geometry Problems? UR - https://www.ijimai.org/journal/bibcite/reference/3432 Y2 - 2025/04/29/07:40:12 L1 - files/5196/Parra et al. - 2024 - Can Generative AI Solve Geometry Problems Strengths and Weaknesses of LLMs for Geometric Reasoning.pdf ER - TY - JOUR TI - A Multifaceted Vision of the Human-AI Collaboration: A Comprehensive Review AU - Puerta-Beldarrain, Maite AU - Gómez-Carmona, Oihane AU - Sánchez-Corcuera, Rubén AU - Casado-Mansilla, Diego AU - López-de-Ipiña, Diego AU - Chen, Liming T2 - IEEE Access DA - 2025/// PY - 2025 DO - 10.1109/ACCESS.2025.3536095 DP - DOI.org (Crossref) VL - 13 SP - 29375 EP - 29405 J2 - IEEE Access SN - 2169-3536 ST - A Multifaceted Vision of the Human-AI Collaboration UR - https://ieeexplore.ieee.org/document/10857320/ Y2 - 2025/04/29/07:40:22 L1 - files/5199/Puerta-Beldarrain et al. - 2025 - A Multifaceted Vision of the Human-AI Collaboration A Comprehensive Review.pdf ER - TY - JOUR TI - Your Prompt is My Command: On Assessing the Human-Centred Generality of Multimodal Models AU - Schellaert, Wout AU - Martínez-Plumed, Fernando AU - Vold, Karina AU - Burden, John AU - A. M. Casares, Pablo AU - Sheng Loe, Bao AU - Reichart, Roi AU - Ó hÉigeartaigh, Sean AU - Korhonen, Anna AU - Hernández-Orallo, José T2 - Journal of Artificial Intelligence Research AB - Even with obvious deficiencies, large prompt-commanded multimodal models are proving to be flexible cognitive tools representing an unprecedented generality. But the directness, diversity, and degree of user interaction create a distinctive “human-centred generality” (HCG), rather than a fully autonomous one. HCG implies that —for a specific user— a system is only as general as it is effective for the user’s relevant tasks and their prevalent ways of prompting. A human-centred evaluation of general-purpose AI systems therefore needs to reflect the personal nature of interaction, tasks and cognition. We argue that the best way to understand these systems is as highly-coupled cognitive extenders, and to analyse the bidirectional cognitive adaptations between them and humans. In this paper, we give a formulation of HCG, as well as a high-level overview of the elements and trade-offs involved in the prompting process. We end the paper by outlining some essential research questions and suggestions for improving evaluation practices, which we envision as characteristic for the evaluation of general artificial intelligence in the future. This paper appears in the AI & Society track. DA - 2023/06/12/ PY - 2023 DO - 10.1613/jair.1.14157 DP - DOI.org (Crossref) VL - 77 SP - 377 EP - 394 J2 - jair SN - 1076-9757 ST - Your Prompt is My Command UR - http://jair.org/index.php/jair/article/view/14157 Y2 - 2025/04/29/07:40:30 L1 - files/5200/Schellaert et al. - 2023 - Your Prompt is My Command On Assessing the Human-Centred Generality of Multimodal Models.pdf ER - TY - JOUR TI - Developing evaluative judgement: enabling students to make decisions about the quality of work AU - Tai, Joanna AU - Ajjawi, Rola AU - Boud, David AU - Dawson, Phillip AU - Panadero, Ernesto T2 - Higher Education DA - 2018/09// PY - 2018 DO - 10.1007/s10734-017-0220-3 DP - DOI.org (Crossref) VL - 76 IS - 3 SP - 467 EP - 481 J2 - High Educ LA - en SN - 0018-1560, 1573-174X ST - Developing evaluative judgement UR - http://link.springer.com/10.1007/s10734-017-0220-3 Y2 - 2025/04/29/07:41:09 L1 - files/5202/Tai et al. - 2018 - Developing evaluative judgement enabling students to make decisions about the quality of work.pdf ER - TY - CONF TI - The Metacognitive Demands and Opportunities of Generative AI AU - Tankelevitch, Lev AU - Kewenig, Viktor AU - Simkute, Auste AU - Scott, Ava Elizabeth AU - Sarkar, Advait AU - Sellen, Abigail AU - Rintel, Sean T2 - CHI '24: CHI Conference on Human Factors in Computing Systems C1 - Honolulu HI USA C3 - Proceedings of the CHI Conference on Human Factors in Computing Systems DA - 2024/05/11/ PY - 2024 DO - 10.1145/3613904.3642902 DP - DOI.org (Crossref) SP - 1 EP - 24 LA - en PB - ACM SN - 9798400703300 UR - https://dl.acm.org/doi/10.1145/3613904.3642902 Y2 - 2025/04/29/07:41:19 L1 - files/5204/Tankelevitch et al. - 2024 - The Metacognitive Demands and Opportunities of Generative AI.pdf ER - TY - JOUR TI - Understanding the relationship between teachers’ pedagogical beliefs and technology use in education: a systematic review of qualitative evidence AU - Tondeur, Jo AU - Van Braak, Johan AU - Ertmer, Peggy A. AU - Ottenbreit-Leftwich, Anne T2 - Educational Technology Research and Development DA - 2017/06// PY - 2017 DO - 10.1007/s11423-016-9481-2 DP - DOI.org (Crossref) VL - 65 IS - 3 SP - 555 EP - 575 J2 - Education Tech Research Dev LA - en SN - 1042-1629, 1556-6501 ST - Understanding the relationship between teachers’ pedagogical beliefs and technology use in education UR - http://link.springer.com/10.1007/s11423-016-9481-2 Y2 - 2025/04/29/07:41:26 ER - TY - JOUR TI - Preparing pre-service teachers to integrate technology in education: A synthesis of qualitative evidence AU - Tondeur, Jo AU - Van Braak, Johan AU - Sang, Guoyuan AU - Voogt, Joke AU - Fisser, Petra AU - Ottenbreit-Leftwich, Anne T2 - Computers & Education DA - 2012/08// PY - 2012 DO - 10.1016/j.compedu.2011.10.009 DP - DOI.org (Crossref) VL - 59 IS - 1 SP - 134 EP - 144 J2 - Computers & Education LA - en SN - 03601315 ST - Preparing pre-service teachers to integrate technology in education UR - https://linkinghub.elsevier.com/retrieve/pii/S0360131511002533 Y2 - 2025/04/29/07:41:35 ER - TY - JOUR TI - Time for a new approach to prepare future teachers for educational technology use: Its meaning and measurement AU - Tondeur, Jo AU - Van Braak, Johan AU - Siddiq, Fazilat AU - Scherer, Ronny T2 - Computers & Education DA - 2016/03// PY - 2016 DO - 10.1016/j.compedu.2015.11.009 DP - DOI.org (Crossref) VL - 94 SP - 134 EP - 150 J2 - Computers & Education LA - en SN - 03601315 ST - Time for a new approach to prepare future teachers for educational technology use UR - https://linkinghub.elsevier.com/retrieve/pii/S0360131515300816 Y2 - 2025/04/29/07:41:41 L1 - files/5208/Tondeur et al. - 2016 - Time for a new approach to prepare future teachers for educational technology use Its meaning and m.pdf ER - TY - JOUR TI - Misinformation: susceptibility, spread, and interventions to immunize the public AU - Van Der Linden, Sander T2 - Nature Medicine DA - 2022/03// PY - 2022 DO - 10.1038/s41591-022-01713-6 DP - DOI.org (Crossref) VL - 28 IS - 3 SP - 460 EP - 467 J2 - Nat Med LA - en SN - 1078-8956, 1546-170X ST - Misinformation UR - https://www.nature.com/articles/s41591-022-01713-6 Y2 - 2025/04/29/07:41:48 L1 - files/5210/Van Der Linden - 2022 - Misinformation susceptibility, spread, and interventions to immunize the public.pdf ER - TY - JOUR TI - Pre-service teacher research: a way to future-proof teachers? AU - Van Katwijk, Lidewij AU - Jansen, Ellen AU - Van Veen, Klaas T2 - European Journal of Teacher Education DA - 2023/05/27/ PY - 2023 DO - 10.1080/02619768.2021.1928070 DP - DOI.org (Crossref) VL - 46 IS - 3 SP - 435 EP - 455 J2 - European Journal of Teacher Education LA - en SN - 0261-9768, 1469-5928 ST - Pre-service teacher research UR - https://www.tandfonline.com/doi/full/10.1080/02619768.2021.1928070 Y2 - 2025/04/29/07:41:57 L1 - files/5212/Van Katwijk et al. - 2023 - Pre-service teacher research a way to future-proof teachers.pdf ER - TY - JOUR TI - Teachers’ beliefs to integrate Web 2.0 technology in their pedagogy and their influence on attitude, perceived norms, and perceived behavior control AU - Van Twillert, Astrid AU - Kreijns, Karel AU - Vermeulen, Marjan AU - Evers, Arnoud T2 - International Journal of Educational Research Open DA - 2020/// PY - 2020 DO - 10.1016/j.ijedro.2020.100014 DP - DOI.org (Crossref) VL - 1 SP - 100014 J2 - International Journal of Educational Research Open LA - en SN - 26663740 UR - https://linkinghub.elsevier.com/retrieve/pii/S2666374020300145 Y2 - 2025/04/29/07:42:04 ER - TY - JOUR TI - Technology Acceptance Model 3 and a Research Agenda on Interventions AU - Venkatesh, Viswanath AU - Bala, Hillol T2 - Decision Sciences AB - ABSTRACT Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre‐ and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations. DA - 2008/05// PY - 2008 DO - 10.1111/j.1540-5915.2008.00192.x DP - DOI.org (Crossref) VL - 39 IS - 2 SP - 273 EP - 315 J2 - Decision Sciences LA - en SN - 0011-7315, 1540-5915 UR - https://onlinelibrary.wiley.com/doi/10.1111/j.1540-5915.2008.00192.x Y2 - 2025/04/29/07:42:14 L1 - files/5215/Venkatesh and Bala - 2008 - Technology Acceptance Model 3 and a Research Agenda on Interventions.pdf ER - TY - JOUR TI - A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies AU - Venkatesh, Viswanath AU - Davis, Fred D. T2 - Management Science AB - The present research develops and tests a theoretical extension of the Technology Acceptance Model (TAM) that explains perceived usefulness and usage intentions in terms of social influence and cognitive instrumental processes. The extended model, referred to as TAM2, was tested using longitudinal data collected regarding four different systems at four organizations (N = 156), two involving voluntary usage and two involving mandatory usage. Model constructs were measured at three points in time at each organization: preimplementation, one month postimplementation, and three months postimplementation. The extended model was strongly supported for all four organizations at all three points of measurement, accounting for 40%–60% of the variance in usefulness perceptions and 34%–52% of the variance in usage intentions. Both social influence processes (subjective norm, voluntariness, and image) and cognitive instrumental processes (job relevance, output quality, result demonstrability, and perceived ease of use) significantly influenced user acceptance. These findings advance theory and contribute to the foundation for future research aimed at improving our understanding of user adoption behavior. DA - 2000/02// PY - 2000 DO - 10.1287/mnsc.46.2.186.11926 DP - DOI.org (Crossref) VL - 46 IS - 2 SP - 186 EP - 204 J2 - Management Science LA - en SN - 0025-1909, 1526-5501 ST - A Theoretical Extension of the Technology Acceptance Model UR - https://pubsonline.informs.org/doi/10.1287/mnsc.46.2.186.11926 Y2 - 2025/04/29/07:42:21 L1 - files/5217/Venkatesh and Davis - 2000 - A Theoretical Extension of the Technology Acceptance Model Four Longitudinal Field Studies.pdf ER - TY - JOUR TI - Participant or spectator? Comprehending the willingness of faculty to use intelligent tutoring systems in the artificial intelligence era AU - Wang, Shanyong AU - Yu, Haotian AU - Hu, Xianfeng AU - Li, Jun T2 - British Journal of Educational Technology AB - Abstract The advancement of technology, especially the development and application of artificial intelligence, has deeply affected the education sector and brought opportunities for pedagogical adaptation. Intelligent tutoring systems, a major application of artificial intelligence in education, have drawn extensive concerns. However, in reality, the penetration rate of intelligent tutoring systems and the enthusiasm of faculty to use are still relatively low. This research examined the determinants of the willingness of faculty to use intelligent tutoring systems. Innovation diffusion theory was the theoretical basis of this research and it was adapted by incorporating perceived trust and experience. To gather data, a cross‐sectional questionnaire survey was performed and structural equation modeling was employed to analyze the data. The findings indicated that relative advantage, compatibility, perceived trust and experience are the contributing determinants of the willingness of faculty to use intelligent tutoring systems, while complexity has no significant effect. Meanwhile, complexity is significantly negatively affected by experience and compatibility. Relative advantage is significantly positively affected by perceived trust but not by complexity. Based on the research findings, relevant recommendations for encouraging faculty to use intelligent tutoring systems were proposed. DA - 2020/09// PY - 2020 DO - 10.1111/bjet.12998 DP - DOI.org (Crossref) VL - 51 IS - 5 SP - 1657 EP - 1673 J2 - Brit J Educational Tech LA - en SN - 0007-1013, 1467-8535 ST - Participant or spectator? UR - https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.12998 Y2 - 2025/04/29/07:42:30 ER - TY - JOUR TI - When Technology Meets Beliefs: Preservice Teachers’ Perception of the Teacher’s Role in the Classroom with Computers AU - Wang, Yu-Mei T2 - Journal of Research on Technology in Education DA - 2002/09// PY - 2002 DO - 10.1080/15391523.2002.10782376 DP - DOI.org (Crossref) VL - 35 IS - 1 SP - 150 EP - 161 J2 - Journal of Research on Technology in Education LA - en SN - 1539-1523, 1945-0818 ST - When Technology Meets Beliefs UR - http://www.tandfonline.com/doi/abs/10.1080/15391523.2002.10782376 Y2 - 2025/04/29/07:42:37 ER - TY - JOUR TI - Classroom misbehaviour management: an SVVR-based training system for preservice teachers AU - Ye, Xindong AU - Liu, Peng-Fei AU - Lee, Xiao-Zhi AU - Zhang, Yi-Quan AU - Chiu, Chuang-Kai T2 - Interactive Learning Environments DA - 2021/01/02/ PY - 2021 DO - 10.1080/10494820.2019.1579235 DP - DOI.org (Crossref) VL - 29 IS - 1 SP - 112 EP - 129 J2 - Interactive Learning Environments LA - en SN - 1049-4820, 1744-5191 ST - Classroom misbehaviour management UR - https://www.tandfonline.com/doi/full/10.1080/10494820.2019.1579235 Y2 - 2025/04/29/07:42:44 ER -