🎯 From Faculty to Futurists—Teaching with AI on Purpose

Step into a lecture hall where algorithms aren’t just powering tools—they’re reshaping how we think, teach, and lead. In a world infused with Artificial Intelligence, today’s educators aren’t just instructors. We’re mentors, designers, advocates, and ethical architects shaping how future leaders interact with—and challenge—technology.

Rethinking Our Role in the AI-Infused Classroom

Over the past year, I’ve had the privilege of collaborating with brilliant colleagues on a framework that reimagines faculty responsibilities in an AI-driven world. Our shared vision centered on a powerful truth: faculty must not only adapt to technology—they must lead its integration with wisdom, purpose, and ethics.

As our ideas continue evolving and a lighter version moves toward publication, I’ve spent time reflecting on how those six faculty roles translate into daily, impactful action—particularly as trends shift and student needs change.

Let’s explore how these roles continue to grow in relevance and what they mean for teaching and learning right now.

🎓 AI Educator From Caution to Confidence

Educators are increasingly called to demystify AI—not as end-all solutions, but as tools requiring critical thought. The role of AI mentor isn’t just about introducing tools. It’s about building digital discernment. As Nguyen et al. (2023) emphasize, transparency is key to fostering trust and equipping students to engage with AI wisely.

✅ Try This: Assign students to document and reflect on AI hallucinations or unexpected outputs. Pair this with a discussion on academic integrity, trust, and tool transparency.

🧠 Designing Better Prompts for Deeper Thinking

Prompt engineering is no longer a technical side-note—it’s central to meaningful learning experiences. Lee and Palmer (2025) found that students who engage in prompt-reflection cycles demonstrate stronger analytical and creative agency. Similarly, Woo et al. (2024) highlight how this process enhances AI self-efficacy and learning autonomy.

✅ Try This: Have students create a “prompt journal,” logging how iterations change responses. Ask them to reflect on what they learned about both the content and the AI.

🧭 Curator of Ethical Tools

Curation today means choosing AI tools that align with course outcomes and community values, including equity, accessibility, and cultural awareness. According to Vinokurova (2021), responsible digital transformation in education requires faculty to critically evaluate the inclusivity and ethical integrity of tools, not just their utility.

✅ Try This: Run semesterly “tool audits” with peers or students. Evaluate for bias, language inclusivity, and user-friendliness. Avoid “shiny object syndrome” and center pedagogical purpose.

⚖️ AI Ethics in Every Classroom

Ethical reasoning shouldn’t live only in philosophy or ethics classes. From marketing to nursing, students must wrestle with fairness, accountability, and power in AI systems. As Akgun and Greenhow (2022) warn, technologies like facial recognition can pose real ethical challenges when used without oversight. Similarly, Antoniak (2023) highlights how AI-generated content can reinforce hidden biases embedded in training data.

✅ Try This: Infuse short, real-world case studies into assignments—like biased hiring platforms or facial recognition in policing. Discuss: Should this AI exist? Who benefits?

🤝 Balancing Tech with Humanity

As AI automates more of the learning process, we must preserve the deeply human aspects of teaching—context, empathy, nuance. Natarajan et al. (2024) advocate for a “human-in-the-loop” design model that ensures AI outputs are reviewed, questioned, and refined by educators for fairness and relevance.

✅ Try This: Review AI-generated feedback with students. Ask them: “What’s missing from this feedback? How would you personalize it?” Guide them toward balanced, critical use.

📊 Teaching Students to Think Beyond the Numbers

Today’s learners must know not just how to read charts, but how to question them. According to Blackmon (2023), students need guidance to navigate the ethical implications of data use, particularly around privacy and digital surveillance. Ologbosere (2023) echoes the call for faculty to help students scrutinize data sources and their social impact.

✅ Try This: Challenge students to cross-check AI-generated insights with peer-reviewed research. Ask: Who created this data? Whose voices are missing?

Betting on Purposeful Practice

When faculty embrace these six evolving roles, we do more than stay current—we stay grounded. We lead with conviction, clarity, and compassion. Whether you’re just beginning your AI journey or mentoring others through theirs, remember: technology doesn’t replace teachers. It reflects our choices.

Your leadership matters now more than ever. Students are watching not only how we use AI—but how we model discernment, integrity, and growth. Let’s keep showing what betting on the AI-infused classroom looks like.

Through our Inspiration Moments, we explore ways to adapt, inspire, and thrive, finding insights and resources that make growth more achievable, even for those juggling many responsibilities. For more inspiration and resources along your journey, visit bettingonme.com. Together, we can make the most of the opportunities before us and create a future full of promise.

Thanks for allowing me to join you on this journey. Until next time, keep thriving and believing that ‘Life happens for you, not to you, to live your purpose.’

Sincerely.

Lynn “Coach” Austin

References

Akgun, S., & Greenhow, C. (2022). Artificial intelligence in education: Addressing ethical challenges in K-12 settings. AI Ethics, 2, 431–440. https://doi.org/10.1007/s43681-021-00096-7

Antoniak, M. (2023). Using large language models with care – AI2 blog. Medium. https://medium.com/ai2-blog/using-large-language-models-with-care-eeb17b0aed27

Blackmon, S. J. (2023). Student privacy and data literacy: An educational opportunity. Change, 55(6), 21–28. https://doi.org/10.1080/00091383.2023.2263189

Lee, D., & Palmer, E. (2025). Prompt engineering in higher education: A systematic review to help inform curricula. International Journal of Educational Technology in Higher Education, 22(1), 1–22.

Nguyen, A., Ngo, H. N., Hong, Y., Deng, B., & Nguyen, B. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28, 4221–4241.

Natarajan, S., Mathur, S., Sidheekh, S., Stammer, W., & Kersting, K. (2024). Human-in-the-loop or AI-in-the-loop? Sustainability, 16(14), 6355–6371.

Ologbosere, O. A. (2023). Data literacy and higher education in the 21st century. IASSIST Quarterly, 47(3/4), 1–7.

Vinokurova, N. (2021). Digital transformation of educational content in the pedagogical higher educational institution. https://doi.org/10.17162/AU.V11I3.713

Woo, D. J., Wang, D., Yung, T., & Guo, K. (2024). Effects of a prompt engineering intervention on undergraduate students’ AI self-efficacy.

About

Lynn F. Austin is an educator, author, emerging AI thought leader, and doctoral candidate who is passionate about inspiring others to reach their highest potential. With a strong foundation in faith and expertise in leadership, personal growth, and AI in higher education, Lynn is dedicated to empowering individuals to embrace challenges, opportunities, and change.

As a speaker, Lynn shares her insights and experiences at schools, conferences, and workshops. As the author of The Newman Tales children’s book series and other business, motivational, and faith-based books, Coach Austin draws from personal experience and professional expertise to motivate readers to join her toward purposeful living along a ‘life happens for me (not to me) to live my purpose’ journey of faith, growth, and inspiration.