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Kong, S.-C., Cheung, W. M.-Y., & Zhang, G. (2023). Evaluating an Artificial Intelligence Literacy Programme for
Developing University Students’ Conceptual Understanding, Literacy, Empowerment and Ethical Awareness. Educational
Technology & Society, 26(1), 16-30. https://doi.org/10.30191/ETS.202301_26(1).0002
16 ISSN 1436-4522 (online) and 1176-3647 (print). DOI 10.30191/ETS. This article of Educational Technology & Society is available under Creative Commons CC-BY- NC-ND 3.0 license (https://creativecommons.org/licenses/by-nc-nd/3.0/). For further queries, please contact Editors at ets.editors@gmail.com.
Evaluating an Artificial Intelligence Literacy Programme for Developing
University Students’ Conceptual Understanding, Literacy, Empowerment
and Ethical Awareness
Siu-Cheung Kong1,2*
, William Man-Yin Cheung2 and Guo Zhang2
1Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong
SAR // 2Centre for Learning, Teaching and Technology, The Education University of Hong Kong, Hong Kong
SAR // siucheungkong@gmail.com // williamcheung@eduhk.hk // gzhang@friends.eduhk.hk
*Corresponding author
(Submitted November 29, 2021; Revised March 26, 2022; Accepted April 25, 2022)
ABSTRACT: Emerging research is highlighting the importance of fostering artificial intelligence (AI) literacy
among educated citizens of diverse academic backgrounds. However, what to include in such literacy
programmes and how to teach literacy is still under-explored. To fill this gap, this study designed and evaluated
an AI literacy programme based on a multi-dimensional conceptual framework, which developed participants’
conceptual understanding, literacy, empowerment and ethical awareness. It emphasised conceptual building,
highlighted project work in application development and initiated teaching ethics through application
development. Thirty-six university students with diverse academic backgrounds joined and completed this
programme, which included 7 hours on machine learning, 9 hours on deep learning and 14 hours on application
development. Together with the project work, the results of the tests, surveys and reflective writings completed
before and after these courses indicate that the programme successfully enhanced participants’ conceptual
understanding, literacy, empowerment and ethical awareness. The programme will be extended to include more
participants, such as senior secondary school students and the general public. This study initiates a pathway to
lower the barrier to entry for AI literacy and addresses a public need. It can guide and inspire future empirical
and design research on fostering AI literacy among educated citizens of diverse backgrounds.
Keywords: Application development, Artificial intelligence literacy, Conceptual framework, Ethical awareness,
University students
1. Introduction
Fostering artificial intelligence (AI) literacy for all citizens has become increasingly crucial, given AI’s potential
to reshape the competitive landscape and its relevance to individuals’ lives and work (Fosso Wamba et al., 2021;
JRC & OECD, 2021; WIPO, 2019). However, few studies have comprehensively examined how and what
exactly to teach to educate citizens of diverse backgrounds.
Most studies of conceptual teaching involve mathematical formulae and programming codes, focusing primarily
on computer science majors and students with programming knowledge (Green, 2021; Pouly et al., 2019;
Stadelmann et al., 2021; Tedre et al., 2021). This approach creates a barrier to literacy amongst the public (Long
& Magerko, 2020). While ethical issues related to AI have received increased attention (Ashok et al., 2022; Jobin
et al., 2019; Kuipers, 2020; Mehrabi et al., 2021; Prunkl, 2022), ethics thus far have rarely been an explicit
component of AI courses (Saltz et al., 2019), and limited information is available on the ethical considerations
covered in AI classes (Garrett et al., 2020).
To fill this research gap and to serve social equity and sustainable development goals (Kong et al., 2021b;
OECD, 2018a; Vinuesa et al., 2020), this study develops an AI programme that focuses on conceptual
understanding, literacy, empowerment and ethical awareness. The literacy development framework presented
here focuses on conceptual building, emphasising project work in application development and enhancing
participants’ awareness of the ethical considerations arising from such work. This study reports the process of
designing, implementing and evaluating this AI literacy programme.
2. Background
We follow the conceptual framework of AI literacy from Kong and Zhang (2021) (see Figure 1). This framework
is comprised of three dimensions: the cognitive dimension; the affective dimension; and the sociocultural
dimension.
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Figure 1. Conceptual framework of AI literacy
The cognitive dimension involves teaching major fundamental AI concepts, particularly machine learning and
deep learning, and how to use them to evaluate and understand the real world. These concepts have profound
societal impacts and are essential to fostering AI literacy (OECD, 2018b; Touretzky et al., 2019; Wong et al.,
2020). By understanding these concepts, learners should be able to evaluate AI artefacts in their lives and the
impacts of the technology, then apply the concepts to understand the AI-permeated world, and form their
attitudes and responses accordingly.
The affective dimension serves to empower participants so they can participate with confidence in the digital
world. It contains four components: grasping the value of AI (Thomas & Velthouse, 1990); perceiving the social
impact of AI (Frymier et al., 1996); believing in one’s ability to produce novel AI ideas and solutions (Paulus &
Brown, 2003); and being confident in one’s competence in engaging with AI (Bandura, 1982). This four-factor
model (meaningfulness, impact, creative self-efficacy and AI self-efficacy) is consistent with the idea of future
literacy from the United Nations Educational, Scientific and Cultural Organization (UNESCO), which aims to
strengthen learners’ imagination and prepare them for change (Yi, 2021). Our initiative aims to develop
participants’ self-confidence in conducting AI-related activities, educate them about AI’s significance and
societal impacts, and enhance their digital creativity.
Finally, the sociocultural dimension concerns the ethical use of AI. Our course followed the ethical principles
outlined in Kong and Zhang (2021), which was built on those stated in the Belmont Report (NCPHS, 1978): (1)
the use of AI should not violate human autonomy; (2) AI’s benefits should outweigh its risks; and (3) AI’s
benefits and risks should be distributed equally. These three principles (autonomy, beneficence/non-maleficence
and fairness) have also been covered by recent AI ethical frameworks (Floridi & Cowls, 2019; HLEG, 2019;
OECD, 2019). As effective guidelines to follow, they serve as the constructs of the ethical consideration survey
detailed in Section 3.4 below.
This multidimensional conceptual framework informs our design, development and evaluation of this literacy
programme. Using this framework, this study focused on the three research questions: (1) Can the AI literacy
programme address AI concepts and literacy? (2) Will participants feel empowered after completing the AI
literacy programme? and (3) Can the AI literacy programme foster participants’ ethical awareness?
3. Methodology
3.1. Course participants
We launched a literacy programme at a Hong Kong university for convenience sampling. A total of 36 university
students from diverse backgrounds joined the programme. Twenty-three were female and thirteen were male.
Seventy-five per cent of the participants were enrolled in bachelor’s degree programmes, including students in
their first, second, third and fourth years of study. The remaining participants were from postgraduate or higher
diploma programmes. As shown in Table 1, the participants came from a wide range of academic backgrounds,
namely Mathematics, Information and Communication Technology, Health Education, Chinese Language
Studies, Psychology, the Sciences (Natural Science & STEM Education), English Language Studies, General
Studies, Music, History, Global and Environmental Studies and Global and Hong Kong Studies.
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Table 1. Distribution of programme participants’ academic backgrounds
3.2. Curriculum
The programme consisted of three courses: Machine Learning, Deep Learning and Developing Artificial
Intelligence Applications. The first two courses develop conceptual understanding of two important AI areas
(Kong & Zhang, 2021; Kong et al., 2021b), thus fostering AI literacy in the cognitive dimension. The third
course further develops AI literacy through applying acquired concepts to project work. This project work in turn
serves as a concrete example to reflect on ethical issues, thus covering the sociocultural dimension. The affective
domain is also enhanced as participants can feel more empowered with more understanding of AI throughout all
three courses.
3.2.1. Course 1: Machine learning
Course 1 introduced the concepts and some related algorithms in both supervised and unsupervised learning. An
overview of AI’s development was first provided, followed by concepts of strong and weak AI. The participants
were encouraged to voice their thoughts on AI’s impact on society.
With this foundation, the participants then discussed the “five steps of machine learning,” together with hands-on
experience using these steps to perform image recognition on an online platform. Afterwards, the participants
learned about two instances of supervised learning, “regression” and “classification,” through examples and
hands-on experience. Finally, this course covered the concept and working principles of unsupervised learning
by applying k-means clustering in a series of case studies (Kong et al., 2021b).
In teaching these concepts, we emphasised conceptual building from the beginning: we used analogies and real- life scenarios rather than programme codes and mathematical formulae to foster students’ conceptual
understanding (Kong et al., 2021b). This allowed the course participants to understand the fundamental concepts
of AI and the rationale that underlie them, thereby simplifying the learning process while deepening their
conceptual understanding.
3.2.2. Course 2: Deep learning
In the same vein, Course 2 developed the participants’ conceptual understanding of deep learning. The course
covered several topics, including data cleaning, data augmentation, neural networks, computer vision, deep
learning and convolution neural networks. Through reviewing the application of the five steps of machine
learning in case studies, the course presented the ideas of data cleaning and data augmentation. The concept of
neural networks was introduced by explaining the ideas of perception, input layers, hidden layers, output layers
and weights, among others. The participants’ understanding was deepened through a lab session of training
neural networks to learn to distinguish different data points within various data sets. The concept of computer
vision was then discussed, as it is commonly applied in neural networks; related applications were shared with
the course participants to provide first-hand experience. The participants were also introduced to convolution
neural networks through a lab session and various discussions. Finally, the participants were given the
opportunity to experience more machine learning tools.
Academic background Number
(percentage)
Academic background Number
(percentage)
Mathematics 8 (22.22%) English Language Studies 2 (5.56%)
Information and Communication Technology 5 (13.89%) General Studies 2 (5.56%)
Health Education 4 (11.11%) Music 2 (5.56%)
Chinese Language Studies 4 (11.11%) History 1 (2.78%)
Psychology 3 (8.33%) Global and Environmental Studies 1 (2.78%)
The Sciences
(Natural Science & STEM Education)
3 (8.33%) Global and Hong Kong Studies 1 (2.78%)
Total 36 (100%)