Generative AI’s capacity to produce credible academic writing disrupts assessment validity in humanities disciplines, where unsupervised written tasks form assessment cornerstones. Current institutional responses, which establish rules about AI use without providing structural enforcement, create an "enforcement illusion" that fails to address core validity challenges. Detection tools remain unreliable, while prohibition-centric approaches neglect AI’s potential as a pedagogical tool (Corbin et al, 2025).

This paper highlights recent examples of assessment redesign that promote the ethical integration of AI within the policy guidelines now adopted by several Australian universities. For example, students might be tasked with using an AI tool to generate a draft essay, then critically revising and annotating it to identify inaccuracies, biases, or gaps in reasoning. This process requires students to apply their disciplinary knowledge and evaluative judgement, reframing AI as a partner in learning rather than a threat, and fostering innovation in assessment design.

Humanities scholars excel in textual analysis, cultural critique, and ethical reasoning, making them well-suited to design assessments that highlight uniquely human skills such as interpretation, contextualisation, and creative synthesis. By embedding these capabilities into assessment tasks, educators can shift the focus to skills that generative AI cannot easily replicate. This not only addresses concerns about assessment validity but also equips students to lead future workplaces where collaboration with AI is common, in an ethical manner. With its interdisciplinary approach to technology and critical practice, the Digital Humanities community is ideally placed to lead this transformation, turning the challenge of assessing complex human abilities into an opportunity to innovate in humanistic education.

In my presentation, I will outline practical strategies for redesigning assessments in the humanities to emphasise human strengths in the age of AI.

Corbin, T., Dawson, P., & Liu, D. (2025). Talk is cheap: why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 1–11. https://doi.org/10.1080/02602938.2025.2503964

Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 49(7), 1005–1016. https://doi.org/10.1080/02602938.2024.2386662

The advent of generative AI (Gen AI), since the release of ChatGPT in late 2022, has significantly altered and challenged the traditional landscape of higher education in the United States. Interestingly, a large proportion of American historians are reluctant to permit the use of Gen AI, often viewing it as a threat to the centrality of essay writing in traditional education. In contrast, I am passionate about integrating AI-enabled alternative assignments into an Asian history classroom. My AI-powered pedagogy originated from students' after-class feedback. Taking their requests for non-traditional or "unessay," assignments into account, I designed an AI-assisted music composition assignment for an Asian history course in the winter of 2025. Students were asked to use the AI platform AI Music Generator (https://aimusic.so) to create a song about China and its global interactions (for example, a song about Chairman Mao and his diplomatic efforts). At the end of the assignment, students were required to submit the AI-generated song alongside critical reflections on the process and its historical context. This AI-powered assignment inspired me to reflect on the relationship between incorporating Gen AI in higher education and the democratization of the classroom. Over the past few years, as a teaching assistant and instructor for history courses, I have noticed a significant preference among students for non-writing assignments. AI-powered platforms and apps can assist instructors in designing and implementing alternative, creative assignments that move beyond traditional essays.

Creative AI is dramatically reshaping the commercial design in marketing, offering increased efficiency, scalability, and new forms of creative expression. However, it also changes traditional workflows, professional identities, and ethical boundaries. While much of the existing research highlights the technical and economic implications of Creative AI, a gap remains in understanding how creative professionals experience and adapt to these shifts.

This study aims to explore how Creative AI is disrupting, evolving, and redefining creative work from the perspective of those directly involved in the commercial design industry. By prioritizing the voices of creative professionals, it investigates changes to creative processes, decision-making, and the broader cultural landscape. This research goes beyond technical and economic discussions by capturing firsthand experiences, concerns, and strategies as practitioners navigate AI-driven transformations.

Utilizing a qualitative methodology, this research conducts in-depth interviews with professionals across advertising, marketing, and branding sectors. Grounded in critical AI and media studies, the analysis is framed at the intersection of technology, creativity, and labor. This approach examines how practitioners integrate, resist, or reconfigure their roles in response to Creative AI, providing a nuanced perspective on this ongoing shift.

The research is expected to reveal a complex landscape in which creative professionals face both opportunities and challenges. Some will embrace Creative AI for its potential to enhance ideation, improve efficiency, and expand creative possibilities. Others may express concerns around job displacement, creative devaluation, and ethical dilemmas tied to authorship, authenticity, and agency. The findings will highlight emerging professional strategies, industry adaptation patterns, and ethical negotiations, offering a detailed view of the evolving creative ecosystem.

This study argues that Creative AI is not merely a tool but an active, relational force within creative production—one that is increasingly co-constitutive of human work and identity. Rather than viewing AI and humans as distinct actors, it explores how they form a dynamic ‘we’ in the creative process. The research interrogates this evolving human-tech entanglement: What does it mean to create with a non-human agent? How do professionals maintain authorship, accountability, and autonomy in this hybrid space? These questions speak to broader concerns about power, creativity, and ethics in AI-augmented work.

By foregrounding industry voices, the study challenges techno-deterministic narratives that frame AI as an inevitable or autonomous force of change. Instead, it highlights how the adoption, integration, and resistance to Creative AI are deeply shaped by human decisions, professional cultures, and broader socio-economic contexts. This humanistic and contingent framing underscores that AI does not evolve in isolation but is negotiated, co-shaped, and redefined by creative practitioners in their everyday work. Ultimately, the study contributes to critical discussions on how to balance AI-driven efficiency with human values, and advocates for policies and educational frameworks that support sustainable, ethical, and human-centered creative practices in the evolving Creative AI landscape.

Generative AI in India made its way into common people’s homes on Cadbury chocolates and Sunfeast biscuits, with Bollywood’s leading actor Shah Rukh Khan starring in AI generated ads for both the companies. The #NotJustACadburyAd, a collaboration between Cadbury, Shah Rukh Khan, and Mondelez India, aims to empower local store owners in India by utilizing AI and machine learning to create personalized advertisements featuring Shah Rukh Khan’s face and voice. The #MyFantasyAdWithSRK allowed fans to experience an AI generated experience of sharing Sunfeast biscuits with their favourite movie star. Both these ads were affected by the dominant narrative structures of marketing bylines and blurbs as well as the classist, almost Brahmanical, ideological systems that inform these narratives structures, from big-brand-ized ads for small businesses to the logic of exclusivity in the trope of the movie date.

The advent of large language models (LLMs) like GPT-4 has transformed text generation, offering innovative possibilities across various sectors, advertisement and marketing being just one area out of many. At the same time, as the example above illustrates, these models are subject to inherent biases, one of which is explored by the AI STORIES project (Rettberg 2024) as narrative bias. In my short paper, I will be describing the theoretical considerations of my ongoing postdoctoral work which posits that this bias emerges from the narrative archetypes embedded in the vast and culturally specific texts that LLMs are trained on, particularly those from English language sources, in my case, specifically Indian narratives. The implications of this bias can be profound, especially when one takes into account the context of cultural diversity and global storytelling practices from non-Western spaces like India.

This short paper will detail work in progress on how Indian narrative traditions, already rooted in a complex network of ancient epics, folktales, religious texts, and contemporary literature, are represented and potentially distorted by LLMs. In doing so, I intend to shed light on the transformation and mutation of culturally rich and diverse Indian narratives as they are processed by AI.