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 notes: What 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.

In recent years, schools started to incorporate artificial intelligence (AI) into computer science/STEAM curricula. However, few validated measurements have been designed to examine how secondary students develop AI literacy and perceive their learning outcomes. AI literacy has been measured from students' knowledge and skill acquisition, and behavior and attitudes. This research aims to develop and validate an instrument to assess AI literacy for secondary students. A questionnaire with 25 items measured in a 5-point Likert scale was created. In a pilot study, the questionnaire was administered to a sample of 363 secondary school students from two different schools in Hong Kong. Confirmatory factor analysis was conducted and grouped into six factors: (1) intrinsic motivation, (2) self-efficacy, (3) behavioral intention, (4) behavioral engagement, (5) know and understand, and (6) use and apply AI. The questionnaire showed a good fit model to support internal consistency reliability in most of the factors, with Cronbach's Alpha levels ranging from .58 to .88. A less-parsimonious model was proposed that help educators measure a wider AI literacy skill set with an acceptable model fit, with Cronbach's Alpha levels ranging from .91 to .94 based on affective, behavorial, cognitive and ethical (ABCE) learning framework. Further studies are needed to confirm the factor structure.

Contribution: While design project courses offer first-year students a practical introduction to engineering, a portion of class time is usually spent on lecturing foundational knowledge instead of practicing engineering design. This article presents a blended design-based learning (bDBL) approach that makes class time more efficient and explores the changes in students’ design competencies and intrinsic motivations. Background: Current approaches to cornerstone courses face challenges, such as heavy faculty involvement and heterogeneity of design projects. bDBL draws on the self-directedness of blended learning and the open-ended nature of design-based learning which may be a worthwhile instructional approach for cornerstone courses. Intended Outcomes: bDBL was applied in a cornerstone course that intended to let students understand what engineers do and motivate them in the field. Students’ design competencies and intrinsic motivations were examined through pre- and post-self-reported surveys. Focus group interviews were conducted to elicit students’ views on bDBL. Application Design: Online self-paced learning modules were created to deliver knowledge-based content. Students transfer what they learned from the online modules through launch-level demos. Then, students spend most of the class time working on team design projects to learn through mistakes and receive first-hand feedback from peers and instructors. Findings: From Fall 2018 to Spring 2020, 201 first-year students experienced bDBL. Quantitative results demonstrated increases in students’ design competencies and intrinsic motivations. Four themes representing both positive and negative views of bDBL were elicited. A conceptual framework that connects the theoretical foundation, design elements, examined effects, and students’ perceptions, is proposed.