‘The Bigger Picture: Reimagining AI Imagery’ is a collaborative, interdisciplinary initiative designed to address public misconceptions and concerns about Artificial Intelligence (AI) (The Bigger Picture, 2024). The project offers a timely opportunity to reshape perceptions of AI through innovative visual storytelling, moving beyond the sci-fi-inspired tropes, limited aesthetics and inaccuracies that dominate current stock image libraries.

The predominance of anthropomorphised robots, glowing brains and binary code fail to convey what AI truly is or how it functions. More critically, the current imagery reinforces misconceptions and restricts wider public understanding of the actual, realistic uses and impact of AI on society (Dihal & Duarte, 2023).

‘The Bigger Picture’ project primarily focuses on promoting Explainable and Communicable AI by fostering informed discussions about the implications of AI in the creative industries and encouraging public participation in the process of artistic creation, interpretation, and critique. Through a series of participatory workshops, artist commissions, public exhibitions, a website and accompanying zine, the project invites communities to engage directly with concepts around how AI is represented in art and media.

A central objective of the project was to promote Explainable AI—the goal of making AI technologies understandable and approachable for non-technical users. By introducing some basic concepts around how explanations for AI are depicted in visual terms such as using decision trees, if-then binary statements or showing feature relevance (Sheridan, Murphy, & O'Sullivan, 2024) participants became familiar with the underlying concepts and inner workings of AI. Participants also explored the broader implications of AI on visual culture by creating images that referenced or were inspired by their own feelings around AI both before and after each workshop and through an uncoding exercise designed to explain what happens during ‘prompting’ in generative AI. Lastly, participants undertook a pictogram design challenge exploring how to visually depict complex themes around how AI works which encouraged critical thinking about the impact of AI on creative practice and on society more widely.

Workshop outputs informed a call for submissions titled “AI is Everywhere” (Call for Submissions: The Bigger Picture, 2024). This call challenged artists and image-makers to depict AI’s current realities rather than relying on dystopian or futuristic clichés. Importantly, submissions were required to be non-AI-generated, emphasising human creativity and reflection. Themes explored included AI’s inherent humanity and its integration into daily life, contrasting with its frequent portrayal as non-human or purely mechanical. During Science Week 2024, ‘The Bigger Picture’ exhibitions showcased thought-provoking artwork on the theme "AI is Everywhere". Selected images were also included in the Better Images of AI online library (Better Images of AI, n.d.), a resource promoting accurate and diverse AI representations.

‘The Bigger Picture’ demonstrates how interdisciplinary collaboration can challenge dominant narratives about AI and empower communities to engage critically with its impact on visual culture and society. By combining XAI concepts with creative expression, ‘The Bigger Picture’ is re-imagining how AI is understood, represented and contextualised in imagery.

References

Better Images of AI. (n.d.). Betterimagesofai.org. https://betterimagesofai.org/images

Dihal, K., & Duarte, T. (2023). Better images of AI: A guide for users and creators. Cambridge and London: The Leverhulme Centre for the Future of Intelligence and We and AI.

Call for Submissions: The Bigger Picture. (2024, October 21). The Research Ireland ADAPT Centre for AI-Driven Digital Content Technology. https://bit.ly/The-Bigger-Picture2024

Sheridan, H., Murphy, E., & O'Sullivan, D. (2024). Human centered approaches and taxonomies for explainable artificial intelligence. Conference papers, (427). Retrieved from https://arrow.tudublin.ie/scschcomcon/427

The Bigger Picture. (2024). The Bigger Picture. https://thebiggerpictureai.com/

Partnerships between students and educators in co-designing the curriculum are becoming more prevalent. However, there is a scarcity of research in the area of assessment co-design (Deeley & Bovill, 2017) and there are few opportunities for full student participation in the assessment process (Dervan, 2018). As students are key stakeholders in their own learning, there is a need to understand effective assessment design from a student perspective, in their own words, if effective practice is to be supported. While the educator is represented as the key decision-maker in assessment of learning, Nieminen (2022) suggests that the validity of this type of assessment might be improved by involving students in assessment design. This article explores the relationship between students as partners (SaP) and assessment co-design in an effort to re-balance power dynamics in higher education (Hassan et al., 2022) and respond to the challenges of an AI future.

The role of student as co-creator seems to be more of an aspiration than reality for many. Some studies include co-design through creation of rubrics, choice of assessment (O’Neill, 2017) and scheduling of assessment. However, there is little evidence in relation to co-design of assessment tasks in the extant literature. Combining a partnership approach with assessment co-design could represent an innovative and authentic alternative to traditional assessment methods. In the context of the rise of generative artificial intelligence, effective assessment co-design could enable educators to reimagine their assessment strategies in a creative way.

This paper presents preliminary results from an assessment co-design workshop where students chose assessment combinations from 16 eportfolio type assessment activities as part of their assessment strategy. They chose the weightings, group or individual and the scheduling of the assessment activities. The educator student assessment (ESA) model used for the workshop was based on the work of Diane Laurillard (2012) who advocates for designing educational experiences that actively involve students in their own learning processes. The ESA model responds to the challenge of AI by emphasising diverse, active learning methods that foster critical thinking and collaboration, skills which AI cannot easily replicate. The co-design workshop allowed the students to tailor the assessment experiences to their individual and group needs. By focusing on production, practice and collaboration assessment activities, the students created, discussed and reflected on content created in balance with using AI as a support tool. Preliminary findings indicate student expectations of clear assessment briefs, assessments involving teamwork and constant feedback on assessment work in order to grow and flourish. As part of their design, they included group and individual tasks, an AI related assessment and a video production assessment. They also highlighted supports needed to complete each task and pointed to the challenges they faced in terms of deciding the timing and mix of assessment weightings.

References

Deeley, S. J., & Bovill, C. (2017). Staff student partnership in assessment: enhancing assessment literacy through democratic practices. Assessment & Evaluation in Higher Education, 42(3), 463-477. https://doi.org/10.1080/02602938.2015.1126551

Dervan, P., (2018). Empowering students to perform an enhanced role in the assessment process: Possibilities and challenges. Transforming our World Through Design, Diversity and Education, 527-538.

Hassan, O., Foley, S., Cox, J., Young, D., McGrattan, C., & Bheoláin, R. N. (2022). Steps to Partnership: Developing, Supporting, and Embedding a New Understanding for Student Engagement in Irish Higher Education [Article]. AISHE-J: The All Ireland Journal of Teaching & Learning in Higher Education(1), 1-19.

Laurillard, D. (2012). Teaching as a design science: building pedagogical patterns for learning and technology. London: Routledge.

Nieminen, J. H. (2022). Assessment for Inclusion: rethinking inclusive assessment in higher education. Teaching in Higher Education, 1-19. https://doi.org/10.1080/13562517.2021.2021395

O’Neill, G. (2017). It’s not fair! Students and staff views on the equity of the procedures and outcomes of students’ choice of assessment methods. Irish Educational Studies, 36(2), 221-236. https://doi.org/10.1080/03323315.2017.1324805

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,…Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Journal of Clinical Epidemiology, 134, 178-189. https://doi.org/https://doi.org/10.1016/j.jclinepi.2021.03.001

While in the classroom we insist on carefully cultivating an awareness of style, genre, and voice, LLMs (Large Language Models) generate "content", even hallucinate it. There, language is instantly levelled into a semi-corporate voice ironically termed "Delvish" by science fiction author Bruce Sterling. Despite its overuse of the verb "to delve", AI is criticised precisely for not straying far enough from its glassy surface, uncritically replicating unexamined biases (Safiya Umoja Noble). Worse still, as information is decontextualised, truncated, and remixed, students increasingly become "format agnostic", reduced to the amorphous term "content creators". For those not already trained prompt engineers with a keen critical eye, the road most travelled is that of least resistance, leading them down to the lowest common denominator where unadventurous style replaces paradigm-shifting substance.

What does non-fungibility in instructional design feel like in a post-AI world? Zines constitute an essential pedagogical tool for media literacy precisely because they model critical recontextualisation. Embedded in a "subaltern counterpublics" (Nancy Fraser) permaculture with a low entry barrier thanks to its playful DIY ethos, zine-making scaffolds a dialogic (Mikhail Bakhtin), oppositional counter/cartography that glitches the deceivingly atemporal re-presentation model of LLMs. Indeed, they can help future-proof education through a multipronged strategy.

Firstly, through Universal Design for Learning, they can be used to help students develop sensory literacies, creating an inclusive learning environment that values the perspectives of learners who are neurodivergent, visually impaired, chronically ill, and/or deal with dyspraxia, dyscalculia, dyslexia, or dysgraphia. As disability activist Imani Barbarin points out, being able-bodied comes with inbuilt planned obsolescence. In a world where haptic feedback is slowly phased out in favour of less accessible hi-tech options even in instances where access is the chief concern (e.g. touch screen elevator panels without buttons, Braille markings or floor announcements), we can steer students towards exploring different modalities, from screen reader versions of digital zines to incorporating tactile markings by using mixed media or engaging with traditional crafts, such as co-creating crochet zines or assembling memorial quilt panels. Secondly, given how resource-intensive AI is, zines offer a low-tech alternative prioritising experimentation over consumption, all while proposing a salvagepunk approach that rejects the inevitability of apocalyptic climate disaster projected by the Anthropocene. Thirdly, actively engaging in a community craft while making space for false starts and repeated course correction with a collaborative ("yes, and") approach can gradually alleviate learned helplessness (Martin Seligman). This is especially important as we recognise that mental health struggles from the pressure of social injustice constitute a public health concern, as they represent an epigenetic cause of autoimmune disorders. Consequently, zines can be used to empower students to seek, build, and maintain inclusive co-creative partnerships, promoting a sustainable approach to lifelong learning through ludic pedagogy.

Bakhtin, M. (1981). The Dialogic Imagination: Four Essays. University of Texas Press. ‌

Brabazon, T. (2015). Enabling University: Impairment, (Dis)ability and Social Justice in Higher Education. Springer International Publishing. ‌

de Bruin-Molé, M. (2021). "Salvaging Utopia: Lessons for (and from) the Left in Rivers Solomon’s An Unkindness of Ghosts (2017), The Deep (2019), and Sorrowland (2021)". Humanities, 10(4). ‌

Fraser, N. (1990). "Rethinking the Public Sphere: A Contribution to the Critique of Actually Existing Democracy". Social Text, 25(25/26), 56–80.

Geronimus, A. (2023). Weathering: The Extraordinary Stress of Ordinary Life on the Body in an Unjust Society. Virago Press.

Kinkaid, E. (2023) "foreword: the desire to counter". you are here: the journal of creative geography. University of Arizona.

Noble, S.U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. New York University Press.

OCLC (Online Computer Library Center). (2004) "Information Format Trends: Content, Not Containers".

As microcosms of society (Battalio, 2005), classrooms act as playgrounds for emerging ideas. Engagements with GenAI in the classroom take a variety of forms from use as learning tools, assessments responses and adaptive learning routes. There is an urgent need for teachers to become acquainted with GenAI in its diverse uses by, for and with students, to determine a rationale for pedagogical and assessment decisions around its use (Chiu, 2023). It is imperative for teachers to start preparing students for a new ethical landscape (Farrelly & Baker, 2023). To do so, they must understand the attitudes of students. This paper presents the outcomes of research conducted in multiple primary schools and with student teachers, using workshops and focus groups. It explores the attitudes of both groups to engagement with GenAI and addresses three research questions;

1. What does appropriate use of GenAI look like for students or student teachers?
2. What are the main immediate or future benefits and/or disadvantages of GenAI for students according to both groups?
3. What gap exists between student and student teacher perceptions and understanding of GenAI?

Our findings indiciate that both groups share concerns for that the same negative endpoint might be reached even if the mode of expression differred. The school context had some impact on the perceptions of GenAI use amongst pupils. This was manifested in student teacher concerns that uptake and use of GenAI in a constructive manner would rely heavily upon parental/guardian input.

The presentation will share its findings and contextualise these along with possible implications for teacher education, and the inclusion of academic integrity and GenAI in current curricula.

References:

Battalio, R. (2005). Setting the stage for a diverse audience. Kappa Delta Pi Record, 42(1), 24–27. https://doi.org/10.1080/00228958.2005.10532081

Chiu, T. K. F. (2023). The impact of Generative AI (GenAI) on practices, policies and research direction in education: a case of ChatGPT and Midjourney. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2023.2253861

Farrelly, T., & Baker, N. (2023). Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice. Education Sciences, 13(11), 1109. https://doi.org/10.3390/educsci13111109

AI and Assistive Technology (AT) are evolving and sometimes overalpping and compliemt the inclusive approach of Universal Design for Learning that advocates an inclusive teaching and learning framework. This melting pot of AI and AT can help not only students with disabilities, who can struggle with time management, motivation, procrastination, focusing and notetaking but these can also help our wider student cohort who can have challenges too. These tools can support students with these challenges but curating these technologies is nesserary so students can buiild them into their learning routines. Harnessing the student experience and voice can begin to compile an authentic list of these AI and AT options and enhance your learning journey. The presentation will be about outlining some of these tools and how they support students and be built into their daily practices. Demonstrations of free tools like Goblin.tools, will be used to show how academic tasks can be broken down, paid AT tools like Glean for notetaking will demonstrate how quizzes are formed from lecture content to test the students memory and comprehension of the lecture content. The overall aim of the session is to impart positive awareness of AI for an acdemic context for students and how this merging of AI and AT is creating more options for incliusion that supports the widening student body in Higher Education.

Virtual exchange (VE) stands for online collaborative learning between groups of students in different cultural contexts and geographical locations combining the deep impact of intercultural dialogue with the broad reach of digital technologies (EVOLVE, 2020). It offers learning benefits - intercultural communicative competence and digital literacy skills development - across the curriculum. In fact, it is an established ‘internationalisation of the curriculum’/’internationalisation at home’ (IaH) strategy in higher education worldwide (O’Dowd & Beelen, 2021). However, VE and VE-based IaH are not inherently equitable and inclusive. Like other forms of online or blended education, they are prone to Western hegemonies and influenced by inequalities in access to and experience with technology, institutional constraints (e.g., lack of support and incentives for educators), gender, race, age, English language dominance, and socio-political and geopolitical challenges (Helm, 2020).

Critical VE (Hauck, 2023) is VE through the social justice and inclusion lens and is informed by critical digital literacy (CDL) which “examines how the operation of power within digital contexts shapes knowledge, identities, social relations, and formations in ways that privilege some and marginalize others” (Darvin, 2017, p. 2). We frame critical AI literacy as a sub-set of CDL and illustrate how CVE provides the ideal educational setting for critical AI literacy skills development for both educators and students allowing them to “gesture towards” (Kerr & Andreotti, 2018; Stein et al., 2020) decolonial VE where participants can engage in thinking “otherwise”(Reljanovic Glimäng, 2022). The approach will be illustrated through several CVE examples where student carried out online collaborative project work that included the critical use and evaluation of GenAI tools and their output.

References:

Darvin, R. (2017). Language, Ideology, and Critical Digital Literacy. In S. Thorne, & S. May (Eds.), Language, Education and Technology. Encyclopaedia of Language and Education (3rd ed.). Springer, Cham

EVOLVE Project Team (2020). The Impact of Virtual Exchange on Student Learning in Higher Education: EVOLVE Project Report. http://hdl.handle.net/11370/d69d9923-8a9c-4b37-91c6-326ebbd14f17Executive

Hauck, M. (2023). From Virtual Exchange to Critical Virtual Exchange and Critical Internationalization at Home. In Diversity Abroad, The Global Impact Exchange. https://www.diversitynetwork.org/GlobalImpactExchange

Helm, F. (2020). EMI, internationalisation, and the digital. International Journal of Bilingual Education and Bilingualism, 23(3), 314-325. https://doi.org/10.1080/13670050.2019.1643823

O’Dowd, R., & Beelen, J. (2021). Virtual exchange and Internationalisation at Home: navigating the terminology, EAIE Blog & podcast. https://www.eaie.org/blog/virtual-exchange-iah-terminology.html

Reljanovic Glimäng, M. (2022). Safe/brave spaces in virtual exchange on sustainability. Journal of Virtual Exchange, 5, 61-81.

Mentorship is considered an important approach in Research Integrity (RI) teaching, e.g. encouraging researchers – the mentees – to act with the highest levels of integrity. However, mentorship is complex, with several known limitations, e.g. a lack of standardisation in mentor training and practice. Recently, a discourse has begun on the benefits of Artificial Intelligence (AI)-based mentors (AIMs), often with authors citing how AIMs may alleviate some of the limitations in current mentorship model. Here, we have focused on the research environment, and how AI-based research mentors (AIRMs) might be used in, and impact on, the area of RI. While the examination of ethical issues with the use of AI across an array of areas is underway, e.g. autonomous vehicles, the identification of the ethical issues with the use of AIRMs is near absent from the literature. Guided by the Anticipatory Technology Ethics (ATE) approach, we have addressed this absence by 1) outlining four plausible future scenarios concerning AIRMs, with a focus on their use and impact in the area of RI, and 2) identifying the ethical issues with such use. Within this talk, we will present the findings from our work to date.

Anderson, M. S., Horn, A. S., Risbey, K. R., Ronning, E. A., De Vries, R., & Martinson, B. C. (2007). What Do Mentoring and Training in the Responsible Conduct of Research Have To Do with Scientists’ Misbehavior? Findings from a National Survey of NIH-Funded Scientists. Academic Medicine, 82(9), 853-860. https://doi.org/10.1097/ACM.0b013e31812f764c

Brey, P.A.E. Anticipating ethical issues in emerging IT. Ethics Inf Technol 14, 305–317 (2012). https://doi.org/10.1007/s10676-012-9293-y

Crean, D., Gordijn, B., & Kearns, A. J. (2023). Teaching research integrity as discussed in research integrity codes: A systematic literature review. Account Res, 1-24. https://doi.org/10.1080/08989621.2023.2282153

Crean, D., Gordijn, B., & Kearns, A. J. (2024). Impact and Assessment of Research Integrity Teaching: A Systematic Literature Review. Science and Engineering Ethics, 30(4), 30. https://doi.org/10.1007/s11948-024-00493-1

Hill, S. E. M., Ward, W. L., Seay, A., & Buzenski, J. (2022). The Nature and Evolution of the Mentoring Relationship in Academic Health Centers. J Clin Psychol Med Settings, 29(3), 557-569. https://doi.org/10.1007/s10880-022-09893-6

Labib, K., Evans, N., Roje, R., Kavouras, P., Reyes Elizondo, A., Kaltenbrunner, W., Buljan, I., Ravn, T., Widdershoven, G., Bouter, L., Charitidis, C., Sørensen, M. P., & Tijdink, J. (2021). Education and training policies for research integrity: Insights from a focus group study. Science and Public Policy, 49(2), 246-266. https://doi.org/10.1093/scipol/scab077

Pizzolato, D., & Dierickx, K. (2023). The Mentor’s Role in Fostering Research Integrity Standards Among New Generations of Researchers: A Review of Empirical Studies. Science and Engineering Ethics, 29(3), 19. https://doi.org/10.1007/s11948-023-00439-z

Abstract

This paper examines the relationship between artificial intelligence and democratic education. AI and other digital technologies are currently being touted for their potential to “democratise” education, even if it is not clear what this would entail (see, e.g., Adel et al., 2024; Kamalov et al., 2023; Kucirkova & Leaton Gray, 2023). By analysing the discourse surrounding educational AI, I distinguish four distinct but interrelated meanings of democratic education: equal access to quality learning, education for living in a democracy, education through democratic practice, and democratic governance of education. I argue that none of these four meanings can render education democratic on its own, and present Dewey’s (1956; 2016) notion of democratic education as integrating these distinct conceptualisations. Dewey emphasises that education needs to provide children with skills and dispositions necessary for democratic living, experience in communication and cooperation, opportunities to codetermine the shape of democratic institutions and education itself, and equal opportunities to participate in learning. By examining today’s commercial AI tools (Holmes & Tuomi, 2022; Khan, 2024), I argue that their emphasis on individualisation of learning, their narrow focus on the mastery of the curriculum, and the drive to automate teachers’ tasks are obstacles to democratic education. I demonstrate that AI deprives children from opportunities to gain experience in democratic living and acquire communicative and collaborative skills and dispositions, while also habituating them to an environment over which they have little or no control, potentially impacting how they will aproach shared problems as democratic citizens. I conclude by outlining some suggestions for making educational AI more in line with a pragmatist notion of democracy and democratic education.

References

Adel, Amr, Ali Ahsan, and Claire Davison. ‘ChatGPT Promises and Challenges in Education: Computational and Ethical Perspectives’. Education Sciences 14, no. 8 (August 2024): 814. https://doi.org/10.3390/educsci14080814.

Dewey, John. The Child and the Curriculum: And The School and Society. University of Chicago Press, 1956.

Dewey, John. Democracy and Education. Gorham, Me: Myers Education Press, 2018.

Holmes, Wayne, and Ilkka Tuomi. ‘State of the Art and Practice in AI in Education’. European Journal of Education 57, no. 4 (2022): 542–70. https://doi.org/10.1111/ejed.12533.

Kamalov, Firuz, David Santandreu Calonge, and Ikhlaas Gurrib. ‘New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution’. Sustainability 15, no. 16 (January 2023): 12451. https://doi.org/10.3390/su151612451.

Khan, Salman. Brave New Words: How AI Will Revolutionize Education (and Why That’s a Good Thing). New York: Viking, 2024.

Kucirkova, Natalia, and Sandra Leaton Gray. ‘Beyond Personalization: Embracing Democratic Learning Within Artificially Intelligent Systems’. Educational Theory 73, no. 4 (2023): 469–89. https://doi.org/10.1111/edth.12590.