Fostering Secondary School Students’ AI Literacy through Making AI-Driven Recycling Bins

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

“…First, the factor ‘career interest’ does not show any significant effect ( p = 0.064). This is consistent with prior STEAM/computer education studies that students may not have a strong interest in career aspirations, as it may be too young for students to imagine their future career (Aschbacher et al., 2014; Ng, Su, et al., 2023). Career counselling and mentoring play an important role in enhancing students' career development for promoting students' interests in STEAM (Stoeger et al., 2013).…”

“…One of the main recommendations by the experts was that, in the cognitive learning dimension, four statements in ‘use and apply’ were combined with ‘evaluate and create’ as students may not identify the differences among applying, evaluating and creating. This aligns with some prior studies which suggest that young learners may struggle to differentiate between the cognitive levels of using and applying AI concepts versus evaluating and creating AI artefacts and solutions (Agarwal, 2019; Ng, Su, et al., 2023; Lemov, 2017). As a result, the ‘use and apply’ dimension that measures students' readiness was eliminated as it was taken into account in other dimensions in the cognitive learning dimension.…”

“…In this study, behavioural learning (ie, collaboration and behavioural commitment) measures students' positive behaviour associated with usage and understanding of AI. Aligned with recent studies (Chiu & Chai, 2020; Ng, Su et al., 2023), this dimension proposed that AI literate students should be collaborators and active and productive learners who are able to facilitate group work, promote meaningful communication and perform positive actions towards using AI technologies.…”

“…(2020)'s study used the concept of ‘AI literacy’ as a measurement variable for scale development; it focused on technological knowledge and skill acquisition. Borrowing the idea of digital literacy, when identifying key competencies, possessing technological proficiency (eg, using digital devices smartly and knowing the knowledge behind) is arguably not the ultimate goal of digital literacy education (Audrin & Audrin, 2022; CEPIS, 2015; Proulx, 1994) as there are other important competencies such as socio‐emotional literacies (Ng, 2012), ethical mindsets (Covello & Lei, 2010; Segun, 2021) and other transferable skills (eg, communication and collaboration) (Audrin & Audrin, 2022; Ng, Su, et al., 2023). Similarly, knowing how to use AI technologies is different from being AI literate.…”

“…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).…”