At the Intersection of AI and Authentic Knowing

We find ourselves at a pivotal moment in the evolution of knowledge construction. As generative artificial intelligence rapidly reshapes our educational landscapes, we face a profound question: How do we embrace these powerful tools while preserving and amplifying the irreplaceable value of diverse human voices and perspectives? This resource emerges from this intersection, offering educators and learning communities a framework for navigating this terrain with intention and care.

The Challenge Before Us

The integration of AI into our learning environments is not merely a technological shift but an epistemological one. When students turn to AI to help articulate their thoughts, summarize complex concepts, or generate creative content, they engage with a system that, despite its impressive capabilities, lacks the lived experience and situated knowledge that gives human thinking its richness and relevance. As bell hooks (1994) reminds us in Teaching to Transgress, education at its best is “the practice of freedom” – a practice that requires authentic voice and critical engagement with one’s own positioning in the world.

The challenge before us is not whether to use AI in education – that technological tide has already arrived – but rather how to use it in ways that strengthen rather than diminish student agency and the diversity of thought that emerges from different social, cultural, and historical positions. This challenge is particularly acute for students from communities whose voices have been historically marginalized within academic discourse. As Patricia Hill Collins (2000) articulates in her work on standpoint theory, knowledge is always constructed from somewhere – it is never neutral or universal but shaped by the social location of its creators.

Positionality as Central to Knowledge Production

Positionality refers to how our social positions, lived experiences, and identities shape our understanding of and engagement with the world. Drawing on Donna Haraway’s (1988) concept of “situated knowledges,” we recognize that all knowledge is partial and influenced by the position from which it emerges. The integration of AI tools into education makes this understanding of positionality not less relevant but more urgent.

In my previous work exploring qualitative methods as pedagogy in psychology education, I found that centering student positionality transformed not only what students learned but how they understood their relationship to knowledge itself. Students who had previously viewed themselves as passive recipients of established truths began to recognize their own capacity to generate meaningful insights from their unique perspectives. This shift – from knowledge consumer to knowledge creator – is fundamental to what Bettina Love (2019) describes as “educational freedom” in which students are empowered to bring their full selves to the learning process.

The power differential in who creates knowledge and whose knowledge is valued has long shaped our educational institutions. As the American Psychological Association acknowledged in its 2021 apology for psychology’s role in promoting racism, academic disciplines have historically privileged certain perspectives while marginalizing others. This history makes the current moment both challenging and promising – AI tools could either reinforce these patterns of exclusion or, if thoughtfully integrated, help disrupt them.

Mattering and Voice in the Age of AI

At the heart of this resource is a commitment to what Love and Muhammad (2020) call “mattering” – ensuring that students understand that their perspectives, experiences, and voices are essential to the collective process of knowledge construction.

The student researchers whose work informs this guide articulated this powerfully in their podcast series exploring AI’s impact on higher education. As one research team noted, “We are the right voice for this conversation” – a recognition that those experiencing technological transformation firsthand bring crucial insights to our understanding of it. Their research revealed both the opportunities AI presents and the crisis of trust it has created between students and faculty, highlighting the need for new frameworks of engagement.

These student researchers found themselves “between worlds” – navigating traditional educational expectations alongside the new realities of AI-enhanced learning. They described using AI tools for brainstorming, structuring assignments, and understanding complex concepts, while simultaneously expressing concern about over-dependency and the potential loss of critical thinking skills. Most importantly, they weren’t simply accepting or rejecting AI but actively trying to figure out how to use it ethically and effectively while preserving authentic learning.

A Qualitative Approach to Understanding AI

This workbook adopts qualitative methodologies as both theoretical framework and practical approach to integrating AI thoughtfully into educational spaces. Qualitative methods, with their emphasis on meaning-making, context, and the value of subjective experience, offer a powerful counterbalance to the quantitative, pattern-recognition strengths of current AI systems.

Throughout this guide, you’ll find activities and frameworks that draw on autoethnography, focus groups, and other qualitative approaches to help students reflect on their experiences, analyze AI’s role in their learning, and develop critical perspectives on knowledge production. These methods create spaces for what Freire (1970) called “conscientization” – the development of critical consciousness about one’s position within systems of power and the potential for transformation.

Using This Guide

The activities and frameworks presented here are designed to be flexible and adaptable across disciplines, with particular relevance for social sciences including psychology, sociology, education, and criminology. Each section builds upon the previous one, but activities can be used independently based on your course needs and contexts.

This is not a prescription for “best practices” in AI integration – the technology and our understanding of its implications are evolving too rapidly for such certainty. Instead, this guide offers starting points for critical engagement, reflection, and conscious decision-making about how AI tools can support rather than supplant the diverse human voices essential to meaningful education.

As we navigate this transformative moment together, let us approach it with both critical awareness and hopeful creativity. The integration of AI into our learning communities presents real challenges to established understandings of authorship, assessment, and knowledge construction. Yet it also offers an opportunity to reimagine education in ways that center student agency, honor diverse perspectives, and foster more equitable knowledge ecosystems.

In the spirit of what Fred Moten (2018) calls “study” – the collective intellectual practice that happens in and beyond formal institutions – I invite you to engage with this guide not as a definitive answer but as a continuing conversation about how we preserve and amplify the irreplaceable value of human diversity in knowledge production, even as we embrace powerful new tools for thinking together.

References

Collins, P. H. (2000). Black feminist thought: Knowledge, consciousness, and the politics of empowerment (2nd ed.). Routledge.

Freire, P. (1970). Pedagogy of the oppressed. Continuum.

Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575-599.

hooks, b. (1994). Teaching to transgress: Education as the practice of freedom. Routledge.

Love, B. L. (2019). We want to do more than survive: Abolitionist teaching and the pursuit of educational freedom. Beacon Press.

Love, B. L., & Muhammad, G. E. (2020). What do we have to lose: Toward disruption, agitation, and abolition in Black education. International Journal of Qualitative Studies in Education, 33(7), 695-697.

Moten, F., & Harney, S. (2013). The undercommons: Fugitive planning & black study. Minor Compositions.