AI Conference Day

As academic integrity, technology adoption, and skill formation become increasingly difficult to separate, education systems are being asked to define responsible AI use in ways that protect meaningful learning and prepare learners for labour markets where work is increasingly AI-mediated. This task is complicated by a deeper shift in the value of foundational and technical skills that education has traditionally helped learners develop. Educators and institutions are therefore managing generative AI disruption at the intersection of education and work, amid growing uncertainty about which skills need deeper practice, which skills need to be developed differently, and how those skills translate into future livelihoods and employment. 

This panel asks how educators should determine which uses of AI constitute legitimate learning support, which should be incorporated into assessed competence, and which should be limited because they weaken foundational skill formation. The panel also examines how these pedagogical decisions shape the skills learners need to thrive in AI-mediated labour markets. 

The panel invites evidence-based contributions on how education systems can set clearer norms for AI use while redesigning learning, assessment, and pedagogy to meet the demands of changing labour markets. We are especially interested in pedagogical and policy approaches that promote balanced and responsible use of AI while remaining attentive to the broader educational value of learning and employability skills demands.

As artificial intelligence becomes deeply embedded in communication systems, it increasingly acts not just as a tool, but as a communicator, reshaping how information is produced, circulated, interpreted, and trusted. This panel will discuss how communication theory and practice must respond to emerging challenges to information integrity from a human-centered perspective. The panel discussion is structured around five interconnected themes: (1) AI-mediated communication, questioning when AI disrupts traditional sender–message–receiver models and blurs boundaries of agency, intentionality, and responsibility, how humans consume AI-generated messages. (2) When generative AI has dramatically lowered the cost and increased the scale and speed of producing mis-and disinformation, how it complicates individuals’ ability to navigate the environment where the boundaries between truth, error, and fabrication are increasingly ambiguous. (3) These challenges are further complicated by platform algorithms and governance structures, which shape what information becomes visible, amplified, or suppressed. What responsibilities should be distributed across platforms, developers, and regulators and users? (4) In AI-mediated environments, trust is no longer confined to media institutions but extends to complex networks of human and non-human actors. Understanding how trust is constructed, eroded, and recalibrated across these relationships is essential for sustaining functioning information ecosystems. (5) Through discussing these four related questions, this panel will turn to a normative and important question: how should humans communicate ethically in the age of AI? It will reflect on what forms of ethical communication practices and integrated literacies are required to preserve integrity in increasingly automated and opaque systems.

Panelists specializing in these thematic areas are warmly welcomed to share their insights into the intersections between technological transformation and human responsibility, highlighting the critical role of human agency in sustaining information integrity in AI-medicated communication ecosystem.

As AI adoption accelerates globally, its impact is increasingly evident across sectors that shape human development and societal progress. However, many low-resource regions—particularly across Africa—continue to face structural barriers, including a limited AI-skilled workforce, that hinder the full realization of AI's potential. Notably, the ability to develop, deploy, and govern AI often rests on skilled talent, effective capacity-building mechanisms, and inclusive educational opportunities. At the same time, AI itself is creating new opportunities to enhance teaching and learning. Universities, training institutions, governments, industry, and communities are experimenting with innovative approaches to AI education and leveraging AI-powered tools to expand access to learning. Yet important questions remain: How can low-resource regions develop a sustainable AI talent pipeline? How can AI education be made more inclusive, locally relevant, and future-oriented? And how can AI technologies be responsibly integrated into education systems to improve learning outcomes? This panel seeks to explore strategies for strengthening AI education ecosystems and harnessing AI to support training and learning towards an inclusive and sustainable future.