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(Image curtesy of Padlet's own AI image generation tool. Prompt: "Generative AI")

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This annotated bibliography examines the subject of Generative Artificial Intelligence (GenAI) through an academic lens.

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(Image prompt: "Annapureddy, R., Fornaroli, A. and Gatica-Perez, D. (2024) Generative AI Literacy: Twelve defining competencies")

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Annapureddy, R., Fornaroli, A. and Gatica-Perez, D. (2024) 'Generative AI Literacy: Twelve defining competencies,' Digital Government Research and Practice [Preprint]. Available at: https://doi.org/10.1145/3685680.

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Annapureddy, R., Fornaroli, A. and Gatica-Perez, D. (2024) 'Generative AI Literacy: Twelve defining competencies

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(Image prompt: "Buselic, V. (2023) Teaching information literacy and critical thinking skills in Chat GPT time")

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Buselic, V. (2023) Teaching information literacy and critical thinking skills in Chat GPT time. International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications. Available at: https://doi.org/10.1109/contesa61248.2023.10384921.

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Buselic, V. (2023) Teaching information literacy and critical thinking skills in Chat GPT time

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(Image prompt: "Chan, C.K.Y. and Hu, W. (2023) Students’ voices on generative AI: perceptions, benefits, and challenges in higher education")

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Chan, C.K.Y. and Hu, W. (2023) 'Students’ voices on generative AI: perceptions, benefits, and challenges in higher education,' International Journal of Educational Technology in Higher Education, 20(1). Available at: https://doi.org/10.1186/s41239-023-00411-8.

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Chan, C.K.Y. and Hu, W. (2023) 'Students’ voices on generative AI: perceptions, benefits, and challenges in higher education'

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(Image prompt: "Jon Dron (2023) The human Nature of Generative AIs and the Technological Nature of Humanity: Implications for education")

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Dron, J. (2023) 'The human Nature of Generative AIs and the Technological Nature of Humanity: Implications for education,' Digital, 3(4), pp. 319–335. Available at: https://doi.org/10.3390/digital3040020.

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Jon Dron (2023) The human Nature of Generative AIs and the Technological Nature of Humanity: Implications for education

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(Image prompt: "Haugsbaken, H. and Hagelia, M. (2024) A new AI literacy for the algorithmic age: prompt engineering or eductional promptization?")

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Haugsbaken, H. and Hagelia, M. (2024) A new AI literacy for the algorithmic age: prompt engineering or eductional promptization?, pp. 1–8. Available at: https://doi.org/10.1109/icapai61893.2024.10541229.

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Haugsbaken, H. and Hagelia, M. (2024) A new AI literacy for the algorithmic age: prompt engineering or eductional promptization?

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(Image prompt: "Kelso, M. (2024) Bridging the gap: Preparing Delaware K-12 teachers for AI Integration")

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Kelso, M. (2024) Bridging the gap: Preparing Delaware K-12 teachers for AI Integration. PhD Dissertation. Wilmington University. Available at: https://www.proquest.com/openview/0eab4a062bf425a66cb41943e907ffe8/1?cbl=18750&diss=y&pq-origsite=gscholar. (Accessed: 22 November 2024).

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Kelso, M. (2024) Bridging the gap: Preparing Delaware K-12 teachers for AI Integration

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(Image prompt: "Kurtz, G. et al. (2024) Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities")

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Kurtz, G. et al. (2024) 'Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities,' Education Sciences, 14(5), p. 503. Available at: https://doi.org/10.3390/educsci14050503.

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Kurtz, G. et al. (2024) Strategies for Integrating Generative AI into Higher Education: Navigating Challenges and Leveraging Opportunities

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(Image prompt: "H. Lyu, Y. Cheng, Y. Fu and Y. Yang (2024) Exploring a LLM-Based ubiquitous learning model for elementary and middle school teachers")

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Lyu, H. et al. (2024) Exploring a LLM-Based ubiquitous learning model for elementary and middle school teachers. 6th International Conference on Computer Science and Technologies in Education (CSTE). Available at: https://doi.org/10.1109/cste62025.2024.00039.

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H. Lyu, Y. Cheng, Y. Fu and Y. Yang (2024) Exploring a LLM-Based ubiquitous learning model for elementary and middle school teachers

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(Image prompt: "Meakin, L. (2024) Exploring the impact of generative artificial intelligence on higher education students’ utilization of library resources")

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Meakin, L. (2024) 'Exploring the impact of generative artificial intelligence on higher education students’ utilization of library resources,' Information Technology and Libraries, 43(3). Available at: https://doi.org/10.5860/ital.v43i3.17246.

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Meakin, L. (2024) Exploring the impact of generative artificial intelligence on higher education students’ utilization of library resources

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(Image prompt: "Wu, Zhang, Carroll (2024) AI governance in Higher Education: Case studies of guidance at Big Ten universities")

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Wu, C., Zhang, H. and Carroll, J.M. (2024) 'AI governance in Higher Education: Case studies of guidance at Big Ten universities,' Future Internet, 16(10), p. 354. Available at: https://doi.org/10.3390/fi16100354.

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Wu, Zhang, Carroll (2024) AI governance in Higher Education: Case studies of guidance at Big Ten universities

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In this research paper, Annapureddy, Fornaroli and Gatica-Perez (2024) propose a competency-based model for expanded digital literacy. They provide twelve competencies necessary for interacting safely and effectively with GenAI – including the competencies of prompt engineering, programming, and AI literacy. They also emphasise the importance of lifelong learning. These authors argue that existing frameworks often fail to properly address the challenges and opportunities of GenAI, including ethical concerns about authorship.

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These twelve competencies are aimed primarily at educators, policymakers, and those in government. The authors are all affiliated with reputable academic institutions, which lends credibility to their work. This article benefits the wider discussion about GenAI in educational settings, by helping redefine ‘information literacy’ in the age of AI. This article’s value also lies in its practicality, as these twelve competencies are concrete and actionable.

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In his conference paper, Buselic (2023) calls for the integration of GenAI into education, arguing that this would enhance, not harm, students’ critical thinking skills and information literacy. While GenAI does hold the potential to spread misinformation, Buselic (2023) argues that educators can use GenAI to teach students to critically evaluate the information they are exposed to. Redesigning curriculums to better integrate GenAI into the learning process will help foster deeper, more critical engagement with learning material.

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Buselic is a university professor with a background in education and IT. This paper is primarily aimed towards other researchers and educators. It contributes to the wider discussion about the need for additional digital literacy education in classrooms. Despite having been published less than a year ago, it has already been cited by four scholarly works. This highlights its relevance and impact.

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These researchers surveyed 399 university students in Hong Kong, analysing their perceptions about GenAI’s role in education. Specifically, the study investigated students’ familiarity with GenAI, and GenAI’s perceived benefits and challenges. Students reported using GenAI frequently – motivated by a desire for personalised support and greater efficiency. Students also reported fears about accuracy, privacy, and the degradation of their critical thinking skills. To better support students, the authors recommend enhanced AI literacy education, along with new institutional policies aimed at minimising the potential for harm.

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This research adds to the wider dialogue about information literacy in the age of GenAI. Its rigorous, mixed-methods design combined a quantitative survey with open-ended interviews. The comprehensive nature of this design strengthens the validity of their findings, as does the fact that their work was peer-reviewed. The intended audience is educators, researchers, and policymakers.

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Dron (2023) argues that GenAI has shifted fundamentally our relationship with technology. GenAI is able to emulate creativity and problem-solving – skills that previously were considered exclusively human. However, by relying on GenAI to create and problem solve for us, we risk lowering our cognitive capacity. Therefore, educational systems should be redesigned to forester greater intrinsic motivation amongst students, so that students will not be tempted to offload work onto AI systems. This can be achieved by emphasising social interactions in classrooms, and by distancing the learning process from the acquisition of grades and academic credentials.

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Dron’s (2023) paper adds a new dimension to the conversation about GenAI, emphasising that educators are responsible for encouraging authentic engagement and academic integrity in classrooms. This suggests that if students are struggling to engage with integrity, the fault may lie in rote learning exercises and an overemphasis on learning outcomes rather than the learning processes. Dron’s background in educational technology lends credibility to his work. So too do the two forward citations this publication has received.

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This conference paper proposes a concept called ‘educational promptization,’ which these authors consider an important aspect of AI literacy. The authors define 'prompt engineering' as the strategies and skills needed to obtain a wide range of output types from GenAI systems. However, they argue that academic conversations about prompt engineering are sometimes misguided, proposing that instead of focusing exclusively on output optimization, educators should foster ‘relational’ and 'symmetrical' AI engagement, critical thinking skills, and a deeper understanding of these technologies. Instead of simple information retrieval, GenAI should be treated as an intelligent, responsive learning companion for students.

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Much like Annapureddy, Fornaroli and Gatica-Perez's (2024) paper, these authors’ practical, evidence-based framework is directed at educators and policymakers, providing concrete strategies for increasing AI literacy in classrooms. By arguing that we should move away from treating GenAI as a mere tool, and should actively foster relationships with it, these authors provide a novel framework for AI literacy in academic contexts, highlighting the intersection between GenAI and education.

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Kelso’s (2024) dissertation examines the readiness of teachers in Delaware – specifically, their readiness for the integration of AI into classrooms. A mixed-methods research design was employed, which included a teacher survey and a literature review. Kelso (2024) reports primarily upon teachers’ frequent lack of AI literacy, which hinders effective GenAI adoption. Therefore, to support teachers and prepare them for the integration of GenAI, Kelso (2024) recommends the adoption of a comprehensive guidance document that addresses these literacy gaps and provides practical guidelines and examples, thereby supporting educators’ professional development.

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Educators and administrators who work in the mainstream K-12 education stream are the dissertation’s intended audience. Kelso (2024) speaks to the intersection between GenAI, education, and information literacy, identifying competencies needed by today’s educators. While the sample size of Kelso’s survey is relatively small, this research highlights the urgent need for the development of new digital competencies. Kelso’s background in educational technology and cybersecurity makes him a credible author on this topic.

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According to Kurtz et al. (2024), integrating GenAI into higher education holds transformative potential, but comes with many potential challenges. To harness positive potential while mitigating those challenges, these authors argue that educators should transition away from lecture-based teaching models, towards student-centred learning paradigms. As part of this transition, educators should focus more on critical thinking, problem-solving, and AI literacy in classrooms. To help educators transition, the researchers propose a four-stage adoption model emphasizing the need for additional faculty training.

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This research article relates to the wider topic of GenAI’s impact on education systems, educators, and students. The authors are affiliated with the Faculty of Instructional Design at Holon Institute of Technology. This makes them credible subject-matter experts on this topic. The intended audience is educators and policymakers working in higher education.

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Lyu et al. (2024) discuss the application of GenAI in elementary and middle school classrooms. They outline an educational model in which GenAI is used to support teachers in a wide range of tasks, including educational resource creation, lesson planning, and professional development. These researchers argue that GenAI can automate many such tasks, while also directly supporting students’ personalised learning. To harness this potential, teachers must acquire effective prompt engineering skills and other digital competencies.

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This conference paper is somewhat conceptual and exploratory. However, it aligns with the findings of other authors included in this annotated bibliography, including Buselic (2023) and Haugsbaken and Hagelia (2024). It also adds to the wider conversation about the use of GenAI in classrooms, and the specific competencies required to use it effectively. The intended audience is educators and researchers.

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Meakin (2024) describes some of the ways in which GenAI is impacting students’ library usage in higher education, arguing that GenAI’s perceived ease-of-use is driving its widespread adoption. Its potential benefits include personalised learning and more efficient information retrieval, but these benefits come with potential risks – including an overreliance on GenAI, and the retrieval of biassed or limited training data. However, Meakin (2024) argues that libraries should not shy away from GenAI. Rather, they must ensure its responsible adoption and integration, to effectively support both students’ learning, and universities’ educational objectives.

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Meakin is a senior lecturer and programme lead at the University of Derby, which lends credibility to this work. The work provides a new dimension to the topic of GenAI in academia, by focusing on academic libraries – where most of the other items included in this annotated bibliography focus on GenAI use in formal classroom settings. The intended audience is that of librarians and university educators.

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This research analyses fourteen universities’ official guidance documents that relate to the use of GenAI, and discusses the ways in which those universities govern responsible GenAI use on campus. The researchers describe the multifaceted approach taken by most of these universities: IT services focus on data security; instructors emphasise pedagogy; libraries prioritise potential research-related implications. In other words, guidance is often strictly role-oriented. While universities often aim to empower students and educators, this role-oriented approach risks generating undue confusion, due to the unnecessary dispersal of information.

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Educators and administrators working in higher education are the intended audience. The article has no forward citations, perhaps because of its young age. However, since its publication in August 2024, this paper has attracted steadily growing online attention and readership, suggesting a reasonable level of impactfulness. Because of the article’s focus, it is directly relevant to the topic of GenAI in classrooms, and of the need for enhanced informational literacy.

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Following recent advances in AI technology, researchers have rushed to study the societal effects of GenAI, and to determine what competencies are needed to use it safely and effectively. This annotated bibliography catalogues and discusses some important works published from 2023-2024, each of which advances our understanding of this timely topic. These bibliographic items evaluate the potential benefits and likely challenges posed by GenAI's use in educational contexts, including classrooms and academic libraries. These resources also probe the ways in which our understanding of ‘information literacy’ is now needing to expand, to accommodate new, AI-related digital competencies. For instance, many researchers argue that students and educators should be taught effective prompt engineering skills, because prompting is what allows users to communicate with GenAI systems.