This collection demonstrates that AI in education requires careful human-centered implementation that preserves authentic learning experiences while leveraging technology's potential to enhance rather than replace meaningful teaching and learning.
Objective: The main goal of this collection was to examine the integration of artificial intelligence in K-12 and postsecondary education through a humanities lens, focusing on ethical, cultural, and pedagogical implications rather than purely technical considerations. The Virginia Commonwealth University Digital Technology in Education working group sought to understand how AI technologies influence human values, experiences, and social structures in educational contexts, with particular attention to equity, accessibility, and the preservation of human-centered learning.
Methods: The research employed diverse methodological approaches across multiple studies and reflective pieces. Case studies documented classroom implementations, including a third-grade teacher using AI-generated imagery to enhance creative writing, dual enrollment instructors integrating text-to-image AI tools for descriptive writing instruction, and language educators exploring AI chatbots as conversation partners. The collection also included theoretical frameworks such as design thinking approaches for K-12 STEM education, policy analysis of AI guardrails, and speculative future scenarios. An interactive UnConference provided participant-driven discussions that were analyzed both thematically and through arts-based inquiry methods to capture educator perspectives and concerns.
Key Findings: The studies revealed several significant insights about AI implementation in educational settings. Third-grade students showed remarkable engagement and improved writing quality when their stories were transformed into AI-generated artwork, with historically marginalized students demonstrating particular success. In dual enrollment composition courses, students from diverse backgrounds outperformed their peers when using AI tools for descriptive writing, suggesting potential equity benefits when AI is thoughtfully integrated. However, the research also identified substantial limitations in AI's ability to support advanced language learning, particularly regarding cultural competence and authentic human interaction. Educators expressed both enthusiasm for AI's potential to personalize learning and legitimate concerns about academic integrity, the erosion of critical thinking skills, and the risk of dehumanizing education. The collection emphasized that successful AI integration requires maintaining "humans in the loop" rather than pursuing full automation.
Implications: The findings contribute significantly to understanding AI's role in education by demonstrating that technology alone cannot solve educational challenges. Instead, the research advocates for human-centered approaches that use AI to augment rather than replace human judgment, creativity, and connection. The collection provides practical frameworks for ethical AI implementation while highlighting the crucial role of teachers in mentoring, nurturing, and monitoring student learning. It suggests that AI can support equity and accessibility when designed with intentionality, but warns against uncritical adoption that might reinforce existing biases or diminish authentic learning experiences. The research positions the humanities as essential for understanding AI's broader implications beyond technical functionality.
Limitations: The collection acknowledges several limitations across its constituent studies. Sample sizes were often small and context-specific, limiting generalizability. Many findings were based on short-term implementations, making long-term impacts unclear. The rapid pace of AI development means some technical discussions may become quickly outdated. Several studies relied on self-reported data from surveys and reflections, introducing potential bias. The focus on specific educational contexts (primarily Virginia schools and universities) may limit broader applicability. Additionally, the collection notes the challenge of keeping pace with rapidly evolving AI capabilities and policies.
Future Directions: The research suggests multiple avenues for continued investigation. Long-term longitudinal studies are needed to understand AI's sustained impact on learning outcomes and student development. Broader implementation studies across diverse educational contexts, socioeconomic backgrounds, and geographic regions would provide more comprehensive insights. The collection calls for continued development of AI literacy curricula and teacher preparation programs. Future research should explore the intersection of AI with other emerging technologies in education, investigate optimal balance between human and AI roles in learning, and develop more sophisticated frameworks for measuring authentic learning in AI-enhanced environments. The authors also emphasize the need for ongoing policy research and the development of ethical guidelines that can evolve with technological advancement.
Title and Authors: "AI in Education: Teaching, Learning, and Thinking with Machines" edited by Mary Strawderman, with contributions from Jesse Senechal, Eric Ekholm, Jason Vest, Moe Greene, Dawn Johnson, Daniel Case, Oscar Keyes, Yaoying Xu, Robert Godwin-Jones, Kait Donahue, Kim Elmore, Haley Hendershot, Amy Corning, Paulina Guerrero, Samaher Aljudaibi, and Justin Thurston.
Published On: August 2025
Published By: VCU Libraries, Virginia Commonwealth University, as part of the VCU Scholars Compass collection supported by the Humanities Research Center.