Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building

Kong, Siu Cheung; Cheung, William Man-Yin; Zhang, Guo

doi:10.1016/j.chbr.2022.100223

Cited by 37 publications

(27 citation statements)

References 21 publications

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“…Our proposed questionnaire items covered affective learning as the first dimension to use AI confidently and self‐efficaciously. Four affective factors (ie, intrinsic motivation, self‐efficacy, career interest and confidence) are hypothesized as important roles in elevating AI literacy, which are consistent with other recent AI education studies (Chiu et al., 2021, Kong et al., 2022; Lin et al., 2021; Su et al., 2022). It relates to students' feelings, which in turn shapes their attitudes towards AI.…”

Section: Discussionsupporting

confidence: 85%

“…Digital literacy education seeks to equip students with the competencies (knowledge, skills and attitudes) in the use of digital technology needed to create learning opportunities, to pursue their careers and interests, to facilitate their work and to contribute to society as responsible citizens (Jin et al., 2020). As AI affects our everyday lives, AI literacy education has been proposed in recent years to facilitate the development of students' attitudes towards AI, perceived knowledge and skills underpinning AI technologies and critical understandings towards ethical implications of AI (Chiu & Chai,2020; Kong et al., 2022; Long & Magerko, 2020; Ng et al., 2021a, 2021b).…”

Section: Discussionmentioning

confidence: 99%

“…In the field of AI literacy, related work has adopted quantitative and qualitative methods to assess students' AI learning outcomes (Ng et al., 2021b). Methods applied to evaluate students' AI knowledge, skills, attitudes and values include pre‐ and post‐knowledge tests, self‐reported questionnaires (eg, Chiu et al., 2021; Kong et al., 2022; Lin et al., 2021; Ng, Wu, et al., 2023), surveys (Druga et al., 2019), curriculum guides (Ng, Leung, et al., 2023), interview tools and project rubrics (Ng, et al., 2022; Zhang et al., 2023). In particular for questionnaires, multiple variables have been considered in prior studies to assess students' AI learning outcomes, such as confidence, readiness, relevance (Chiu et al., 2021; Xia et al., 2022), behavioural intention (Chai et al., 2021), learning perceptions towards AI (Williams et al., 2022), learning motivation (Chiu et al., 2021; Ng, Leung, Su, Yim, et al., 2023; Xia et al., 2022), attitudes and career aspirations (Zhang et al., 2023) and fun, behavioural engagement, hands‐on interactivity, futuristic thinking, interdisciplinary thinking (Sakulkueakulsuk et al., 2018), social good (Ng, Su, et al., 2023; Selwyn & Gallo Cordoba, 2021) and ethical learning (Kong et al., 2022; Lin & Van Brummelen, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Ng,

Wu,

Leung

et al. 2023

Brit J Educational Tech

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Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self‐efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32‐item self‐reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first‐ and second‐order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four‐factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy. Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy. Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes. What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions. Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy. Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy. Practitioners are able to use the AILQ to assess students' AI literacy development.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Ng,

Wu,

Leung

et al. 2023

Brit J Educational Tech

View full text Add to dashboard Cite

show abstract

“…Although students need increased exposure to such tools (Harrison & Ajjan, 2019; Kong et al., 2022), this is unhelpful without students' increased awareness of the importance of factual and relevant information and the limitations of such tools (Desha et al., 2021). Without increasing students' awareness of the nature of such information, students are unlikely to hone their problem‐solving skills or creative use of knowledge in new settings using language models (Desha et al., 2021; Ryan & Aasetre, 2021).…”

Section: Resultsmentioning

confidence: 99%

Exploring the impact of language models, such as ChatGPT, on student learning and assessment

Zirar

2023

Review of Education

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Recent developments in language models, such as ChatGPT, have sparked debate. These tools can help, for example, dyslexic people, to write formal emails from a prompt and can be used by students to generate assessed work. Proponents argue that language models enhance the student experience and academic achievement. Those concerned argue that language models impede student learning and call for a cautious approach to their adoption. This paper aims to provide insights into the role of language models in reshaping student learning and assessment in higher education. For that purpose, it probes the impact of language models, specifically ChatGPT, on student learning and assessment. It also explores the implications of language models in higher education settings, focusing on their effects on pedagogy and evaluation. Using the Scopus database, a search protocol was employed to identify 25 articles based on relevant keywords and selection criteria. The developed themes suggest that language models may alter how students learn and are assessed. While language models can provide information for problem‐solving and critical thinking, reliance on them without critical evaluation adversely impacts student learning. Language models can also generate teaching and assessment material and evaluate student responses, but their role should be limited to ‘play a specific and defined role’. Integration of language models in student learning and assessment is only helpful if students and educators play an active and effective role in checking the generated material's validity, reliability and accuracy. Propositions and potential research questions are included to encourage future research.

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“…However, they may not have acquired AI literacy, including understanding the concepts and principles underlying these technologies and using them in an ethical and responsible manner (e.g. Kong et al, 2021Kong et al, , 2022Long and Magerko, 2020;Ng et al, 2021;Su and Yang, 2022). To keep pace with the advances in AI use within education, it is particularly imperative that teachers, parents, and children all learn to develop and apply AI literacy appropriately.…”

Section: Applying the Power Principles To Guide The Appropriate Use O...mentioning

confidence: 99%

Artificial intelligence as a double-edged sword: Wielding the POWER principles to maximize its positive effects and minimize its negative effects

Chen

Lin

2023

Contemporary Issues in Early Childhood

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Advances in artificial intelligence (AI) over the last few decades are transforming the world, pervading nearly all sectors of society, including education, and many aspects of life. In the education discourse, interest in artificial intelligence has sparked various reactions and controversies—everything from appreciation for AI's capabilities to make teaching and student learning more efficient and effective to apprehension about their potential overuse and misuse. In this article, the authors discuss how artificial intelligence is a double-edged sword in early childhood education by presenting some of its positive effects (personalized learning, personalized interactive support, and increased accessibility to broadened learning experiences) and negative effects (overuse and misuse). Considering that young children are growing up in a nearly AI-ubiquitous world and are likely exposed to AI-powered tools, the authors propose applying the POWER (purposeful, optimal, wise, ethical, responsible) principles to maximize the benefits and minimize the drawbacks of AI use. Additionally, the authors recommend the integration of the POWER principles into AI literacy as an imperative for promoting the appropriate use of AI-powered tools.

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Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building

Cited by 37 publications

References 21 publications

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Design and validation of the AI literacy questionnaire: The affective, behavioural, cognitive and ethical approach

Exploring the impact of language models, such as ChatGPT, on student learning and assessment

Artificial intelligence as a double-edged sword: Wielding the POWER principles to maximize its positive effects and minimize its negative effects

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