This project builds upon the idea that modern-day AI technologies bring a host of new challenges related to the technologies themselves, the environment in which the technologies are used, and the society interacting with the technologies. AI and robotics are transformative fields pushing the boundaries of what we know about and expect from technology. Unlike other technologies, AI and robotics possess unique features that disrupt established norms.

Most people who use AI and robots everyday do not know much about the technology they are interacting with, which is not much different than many people who do not know about the computers they use everyday, but it is newer and scarier. A widespread lack of technical knowledge can hinder the public’s ability to trust in and accurately understand, use, and evaluate AI capabilities and limitations.

AI often blurs traditional legal distinctions between a person and an instrument, and judges frequently hold a limited view of AI, perceiving AI as a tool without spontaneity, despite their advanced and sometimes unpredictable capabilities.

Narratives are a powerful tool in policy design, particularly in fields like artificial intelligence, where technical complexity can alienate the public. They provide a framework for simplifying and contextualizing complex issues, making them accessible to diverse audiences. For instance, narratives about autonomous vehicles reducing accidents or AI revolutionizing healthcare make these technologies’ benefits relatable, while stories highlighting risks, such as job displacement or algorithmic bias, bring attention to critical ethical dilemmas. This is especially important for AI, where public perception often hinges on high-profile narratives, such as concerns about surveillance or the promise of transformative innovation. Policymakers can leverage narratives to counter misinformation, build trust, and foster transparency, ensuring that public engagement is informed and meaningful. 

Beyond shaping public perception, narratives facilitate communication between technical experts, policymakers, and diverse stakeholders. They act as a bridge, translating complex technical concepts into real-world implications that resonate with non-technical audiences. For example, explaining algorithmic decision-making in the context of healthcare outcomes can demystify AI’s role and limitations, fostering interdisciplinary collaboration essential for robust policy design. 

Since the introduction of a relatively new narrative approach called the Narrative Policy Framework (NPF) in 2010, we have seen a rise in narrative research and a growing community of scholars applying the NPF in their research. The NPF form contains four core structural elements:

• Characters are entities who act or who are acted upon in a policy narrative. A villain is the entity causing a policy problem (harm). A victim is harmed by the villain. A hero offers relief for the policy problem and presumes to fix the problem. 

• Setting is the policy context or the space in which the policy narrative happens. The setting can be viewed either as the spatial context of immediate surroundings (e.g., public spaces for robots) or a broader social-economic-geographic political context. Setting consists of information such as policy phenomena, evidences, and when the policy narrative takes place.

• Plot establishes the relationship between the characters, the setting, and the sequence of events. Further, it specifies characters’ actions, their motives, and conflicts between them.

• Moral is the solution to the policy problem that is usually cast as the action of the hero to protect the victim or create a beneficiary.

Second component of the NPF, the focus on "content," is to infuse meaning to narrative elements and creates policy realities through incorporating narrators’ belief system and narrative strategies. 

Narratives help integrate interdisciplinary perspectives into AI literacy education, a critical need in the field. AI technologies have far-reaching societal implications, including in areas like justice, healthcare, and environmental sustainability. Narratives allow course designers to highlight these intersections by presenting case studies that resonate across disciplines. For example, a module on predictive policing can incorporate narratives from marginalized communities to explore issues of fairness and equity, fostering critical thinking and ethical reasoning. 

Incorporating narratives into AI literacy modules also enhances student engagement by making the content relatable and emotionally resonant. Research suggests that storytelling improves retention and understanding by fostering connections between learners and the material. For instance, embedding narratives about the societal impact of AI in climate modeling can inspire students to explore how their technical skills can contribute to addressing global challenges. Ultimately, narratives are indispensable for creating comprehensive, interdisciplinary, and engaging undergraduate modules that prepare students to navigate and shape the future of AI responsibly.