AI literacy in K-12: a systematic literature review

International Journal of STEM Education, Journal Year: 2023, Volume and Issue: 10(1)

Published: April 19, 2023

Abstract The successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest training citizens AI. Education systems now need train students at the K-12 level live society where they must interact with Thus, AI literacy is pedagogical cognitive challenge level. This study aimed understand how being integrated into education worldwide. We conducted search process following systematic literature review method using Scopus. 179 documents were reviewed, two broad groups approaches identified, namely learning experience theoretical perspective. first group covered experiences technical, conceptual applied skills particular domain interest. second revealed that significant efforts are made design models frame proposals. There hardly any assessed whether understood concepts after experience. Little attention been paid undesirable consequences an indiscriminate insufficiently thought-out application A competency framework required guide didactic proposals designed by educational institutions define curriculum reflecting sequence academic continuity, which should be modular, personalized adjusted conditions schools. Finally, can leveraged enhance disciplinary core subjects integrating teaching those subjects, provided co-designed teachers.

Language: Английский

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Exploring the AI competencies of elementary school teachers in South Korea

Computers and Education Artificial Intelligence, Journal Year: 2023, Volume and Issue: 4, P. 100137 - 100137

Published: Jan. 1, 2023

Although the importance of K–12 artificial intelligence (AI) education grows, lack teacher readiness hinders integration AI in schools. To address this issue, study aimed to explore South Korean elementary school teachers' experiences teaching curricula and examine their competencies. A survey interviews were conducted with 67 teachers who have been working AI-leading schools Korea. The results indicated that least confident content knowledge, followed by technological knowledge pedagogical relevant AI. Additionally, 13 revealed five themes regarding education: (1) emphasizing instructional design education; (2) redesigning learning environment promote experiences; (3) lowering anxiety acknowledging limitations knowledge; (4) extending based on computer science (CS) principles; (5) acquiring literacy codes, data, technologies, ethical issues. Based findings, 22 competencies for derived categorized (TPACK) framework. provide a practical framework acquire necessary skills education. contributes understanding practices Korea revealing teachers’ perspectives identifying essential practicing

Language: Английский

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“To Use or Not to Use?” A Mixed-Methods Study on the Determinants of EFL College Learners’ Behavioral Intention to Use AI in the Distributed Learning Context

The International Review of Research in Open and Distributed Learning, Journal Year: 2024, Volume and Issue: 25(3), P. 158 - 178

Published: Aug. 26, 2024

Artificial intelligence (AI) offers new possibilities for English as a foreign language (EFL) learners to enhance their learning outcomes, provided that they have access AI applications. However, little is written about the factors influence intention use in distributed EFL contexts. This mixed-methods study, based on technology acceptance model (TAM), examined determinants of behavioral among 464 Chinese college learners. As quantitative data, structural equation modelling (SEM) approach using IBM SPSS Amos (Version 24) produced some important findings. First, it was revealed perceived ease significantly and positively predicts usefulness attitude toward AI. Second, contrary TAM assumptions, does not predict either or Third, mediation analyses suggest has significant positive impact students’ through AI, rather than usefulness. qualitative semi-structured interviews with 15 learners, analyzed by software MAXQDA 2022, provide nuanced understanding statistical patterns. study also discusses theoretical pedagogical implications suggests directions future research.

Language: Английский

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Teachers’ AI-TPACK: Exploring the Relationship between Knowledge Elements

Sustainability, Journal Year: 2024, Volume and Issue: 16(3), P. 978 - 978

Published: Jan. 23, 2024

The profound impact of artificial intelligence (AI) on the modes teaching and learning necessitates a reexamination interrelationships among technology, pedagogy, subject matter. Given this context, we endeavor to construct framework for integrating Technological Pedagogical Content Knowledge Artificial Intelligence Technology (Artificial Intelligence—Technological Knowledge, AI-TPACK) aimed at elucidating complex interrelations synergistic effects AI pedagogical methods, subject-specific content in field education. AI-TPACK comprises seven components: (PK), (CK), AI-Technological (AI-TK), (PCK), (AI-TCK), (AI-TPK), itself. We developed an effective structural equation modeling (SEM) approach explore relationships teachers’ knowledge elements through utilization exploratory factor analysis (EFA) confirmatory (CFA). result showed that six all serve as predictive factors variables. However, different varying levels explanatory power relation AI-TPACK. influence core (PK, CK, AI-TK) is indirect, mediated by composite (PCK, AI-TCK, AI-TPK), each playing unique roles. Non-technical have significantly lower teachers compared related technology. Notably, (C) diminishes PCK AI-TCK. This study investigates within its constituent elements. serves comprehensive guide large-scale assessment AI-TPACK, nuanced comprehension interplay contributes deeper understanding generative mechanisms underlying Such insights bear significant implications sustainable development era intelligence.

Language: Английский

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Investigating AI-based academic support acceptance and its impact on students’ performance in Malaysian and Pakistani higher education institutions

Education and Information Technologies, Journal Year: 2024, Volume and Issue: 29(14), P. 18695 - 18744

Published: March 14, 2024

Language: Английский

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AI-Powered Language Translation for Multilingual Classrooms

Advances in educational technologies and instructional design book series, Journal Year: 2024, Volume and Issue: unknown, P. 29 - 46

Published: June 3, 2024

The revolutionary effects of AI-powered language translation technologies on multilingual classrooms in the modern educational environment are explored this chapter proposal. It starts with a historical investigation and follows development AI translation, highlighting innovations neural networks machine learning models that improve efficiency accuracy. After that, focuses deploying tools contexts. To support study, real-world case studies used to examine platforms apps already use thoroughly. accessibility for non-native speakers foster an equal students different linguistic origins is critically discussed. also looks at how may help teachers from cultural backgrounds communicate one another, which can promote inclusive environment.

Language: Английский

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“ENHANCING LEARNING POTENTIAL: INVESTIGATING MARKETING STUDENTS’ BEHAVIORAL INTENTIONS TO ADOPT CHATGPT”

Marketing Education Review, Journal Year: 2024, Volume and Issue: 34(3), P. 201 - 234

Published: Jan. 2, 2024

The rapid proliferation of the Internet has sparked a resurgence attention toward function novel artificial intelligence technologies in higher education. effective adoption recent advancements human-computer interaction is crucial for inclusive education and innovation, resulting sustainable socioeconomic growth development. Therefore, present investigation focuses to examine various factors that exert an influence on acceptance use ChatGPT by marketing students. research's significance lies integrating concept system flexibility into Unified Theory Acceptance Use Technology (UTAUT) model. An adapted questionnaire was administered gather information, statistical procedures were conducted 309 valid responses. study's results revealed habit most significant predictor behavioral intention, with performance expectancy effort following closely behind. However, research shows perceived risk not vital factor students, as they exhibit heightened sense control regulating their online behavior. Besides, implications presented this study hold great policymakers, educators, top-level management personnel within institutions evaluate update existing policies accommodate integration AI tools like

Language: Английский

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Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach

Education and Information Technologies, Journal Year: 2024, Volume and Issue: 29(13), P. 17169 - 17209

Published: Feb. 21, 2024

Language: Английский

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Understanding continuous use intention of technology among higher education teachers in emerging economy: evidence from integrated TAM, TPACK, and UTAUT model

Studies in Higher Education, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 20

Published: April 23, 2024

This paper presents a new model of integrated technology continuance (ITCM) to explain teachers' Continuous Use Intention (CUI) in the higher education institutions (HEIs). Drawing constructs from prominent adoption models like TAM, UTAUT, and incorporating theory TPACK (Technological Pedagogical Content Knowledge), research is developed tested. An online survey was carried out gather data 573 teachers teaching HEIs an emerging economy using purposive sampling method. Data collected evaluated utilizing partial least squares structural equation modeling (PLS-SEM). Analysis establishes applicability ITCM predicting CUI among teachers, with explanatory power 60.4%. The study also highlights positive influence facilitating conditions management support on which has favorable impact self-efficacy, perceived usefulness, ease use. Additionally, use, social major HEIs. provides insights into factors influencing integration technological innovations pedagogy classroom settings achieve some key tasks sustainable development goal 4 (SDG4). Future directions implications have been proposed considering findings.

Language: Английский

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Pre-Service Teachers’ GenAI Anxiety, Technology Self-Efficacy, and TPACK: Their Structural Relations with Behavioral Intention to Design GenAI-Assisted Teaching

Behavioral Sciences, Journal Year: 2024, Volume and Issue: 14(5), P. 373 - 373

Published: April 29, 2024

Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability impact school education. It is crucial investigate pre-service teachers’ viewpoints effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 teachers were analyzed explore predictors of behavioral intention design Gen AI-assisted teaching. Based on Unified Theory Acceptance and Use Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), anxiety, technology self-efficacy. Our findings revealed that social influence, performance expectancy significantly predicted GenAI-assisted However, effort facilitating conditions not statistically associated with intentions. These offer significant insights intricate relationships between influence perspectives intentions regarding technology.

Language: Английский

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