Preparing Instructors for AI Integration: Professional Learning Strategies

 

 

 

By Simone Conceição

 

As artificial intelligence (AI) becomes a transformative force in education, instructors across all disciplines and levels—especially in adult and continuing education—must be prepared to integrate AI tools responsibly, effectively, and equitably into their teaching practices. However, the rapid pace of technological change has left many educators uncertain about how to begin, what tools to use, and what ethical considerations to address.

 

This post outlines key professional learning strategies that institutions and educators can adopt to build confidence, competence, and critical awareness around AI in teaching and learning.

 

Why Faculty Development Is Critical for AI Integration

Effective AI integration doesn’t begin with technology—it begins with pedagogy. According to Zawacki-Richter et al. (2019), most AI research in higher education has focused on technological capabilities, often overlooking the pedagogical and professional needs of instructors. Without appropriate support, educators may underutilize tools, reinforce bias, or resist AI altogether.

 

Adult educators must cultivate both technical fluency and andragogical insight when navigating AI, ensuring that use of these tools aligns with adult learning principles such as relevance, autonomy, and critical reflection.

 

Professional Learning Strategies for AI Integration

1. Start with Foundational AI Literacy. Instructors need a working understanding of how AI functions, what types of tools are available, and how algorithms use data to generate outcomes.

• Offer self-paced modules or short workshops on AI basics.
• Use plain-language explanations and real-world examples.
• Introduce key terms such as machine learning, natural language processing, and generative AI.

 

Goal: Reduce fear and foster curiosity by demystifying the technology.

 

2. Contextualize AI within Pedagogical Practice. AI should be introduced not as a standalone innovation, but as a tool that supports learning goals.

• Explore case studies showing how AI enhances feedback, scaffolding, or engagement.
• Encourage faculty to align AI use with course outcomes, not convenience alone.
• Include discussions on AI’s role in formative assessment and inclusive practices.

 

Goal: Ensure instructional use is meaningful and learner-centered.

 

3. Encourage Exploration and Experimentation. Hands-on experience builds confidence. Provide protected time and space for faculty to explore AI tools and assess their potential.

• Organize low-stakes “sandbox” sessions.
• Host faculty learning communities focused on experimentation.
• Provide small grants or micro-credentials for course redesign projects that integrate AI.

 

Goal: Empower instructors to learn by doing in a supportive environment.

 

4. Facilitate Ethical and Critical Discussions. Professional learning should include ethical inquiry—not just technical training.

• Discuss issues such as data privacy, algorithmic bias, authorship, and transparency.
• Introduce frameworks like those from Holmes et al. (2022) for ethical AI in education.
• Encourage reflection on how AI may impact learner equity and agency.

 

Goal: Promote responsible, reflective AI use aligned with educational values.

 

5. Model AI Use in Faculty Development. Lead by example: integrate AI tools into the professional learning experience itself.

• Use generative AI to personalize workshop content or simulate scenarios.
• Demonstrate how AI can streamline feedback or facilitate knowledge construction.

 

Goal: Show—not just tell—how AI can be pedagogically productive.

 

Institutional Support for Sustainable AI Integration

In addition to individual professional development, institutions should:

• Create cross-functional AI task forces involving faculty, learning designers, and IT staff.
• Develop guidance on appropriate and transparent AI use, including academic integrity policies.
• Recognize and reward faculty who engage in innovative, ethical AI practices.

 

Embed AI into broader digital transformation strategies, ensuring it complements—not disrupts—existing instructional and student support systems.

 

---

Conclusion: Building a Culture of AI Readiness

Preparing instructors for AI integration is not just a technical challenge—it is a professional learning imperative. Through sustained, collaborative, and values-driven professional development, educators can harness AI’s potential while remaining grounded in human-centered teaching.

 

At the AI Literacy Forum in the Adult Learning Exchange Virtual Community, faculty developers and educators are invited to share practices, ask questions, and collaborate on creating inclusive, ethical, and engaging AI-enhanced learning environments. Moderated by Drs. Simone Conceição and Lilian Hill, the forum is a space for growing collective capacity in the age of AI.

---

 

References

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Santos, O. C. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(4), 575–617. https://doi.org/10.1007/s40593-021-00239-1

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0

Data Privacy and Security for Adult Learners in AI Systems

 

By Lilian H. Hill

 

Artificial intelligence (AI) systems are now embedded in many adult learning environments, including learning management systems, adaptive learning platforms, writing and tutoring tools, learning analytics dashboards, and virtual advising systems. These technologies promise personalization, efficiency, and expanded access to learning. At the same time, they raise critical concerns about data privacy and security, especially for adult learners navigating education alongside their professional, familial, and civic responsibilities.

 

Understanding how AI systems collect, analyze, store, and protect learner data is essential for fostering trust, supporting ethical practice, and empowering adult learners to make informed decisions about their participation in AI-enabled learning environments.

 

Why Data Privacy Is Especially Important for Adult Learners

Data privacy for adult learners in AI systems hinges on data minimization, strong security (encryption, access controls), and transparency, ensuring only necessary data is collected and used ethically, with learners retaining control, while security measures like multi-factor authorization, encryption, and regular audits protect sensitive information from breaches, acknowledging that user inputs in GenAI can train the models, requiring caution about sharing private data. 

 

Adult learners differ from traditional-age students in ways that heighten the stakes of data privacy. Many adult learners are employed professionals whose learning data may intersect with workplace evaluations, licensure requirements, or career advancement. Others may be returning to education after long absences or engaging in learning to reskill in rapidly changing labor markets. These contexts make confidentiality, consent, and control over personal information particularly important (Kasworm, 2010; Rose et al., 2023).

 

AI systems collect extensive data, including demographic information, learning behaviors, written assignments, discussion posts, performance metrics, and engagement patterns. When these data are inadequately protected or used beyond their original purpose, adult learners may face risks such as loss of privacy, data misuse, reputational harm, or unintended surveillance (Azevedo et al., 2025; Prinsloo & Slade, 2017).

 

How AI Systems Use Learner Data

AI-driven learning technologies rely on data to function. Algorithms analyze learner inputs to personalize content, generate feedback, predict performance, or automate decision-making processes. While these capabilities can support learning, they also introduce complexity and opacity. Learners may not know what data are collected, how long they are retained, or how algorithmic decisions are made (Zuboff, 2019).

 

From an ethical perspective, transparency is critical. Responsible AI systems should clearly communicate what data are collected and why, how data are processed and analyzed, whether data are shared with third parties, how long data are retained, and what rights learners must access, correct, or delete their data. Without transparency, learners are asked to trust systems they may not fully understand, undermining autonomy and informed consent (Floridi et al., 2018).

 

Data Security Risks in AI-Enabled Learning

Beyond privacy, data security refers to the technical and organizational safeguards that protect information from unauthorized access, breaches, or misuse. Educational institutions and technology vendors increasingly store learner data in cloud-based systems, which can be vulnerable to cyberattacks if not adequately secured (Azevedo et al., 2015; Means et al., 2020).

 

Despite the rapid adoption of AI tools, institutional guidance on their responsible integration into higher education remains uneven. Where policies exist, they differ substantially in scope, enforceability, and levels of faculty involvement, leaving many educators uncertain about what is permitted, encouraged, or restricted (Azevedo et al., 2024). As a result, institutions face an increasing imperative to develop AI policies that not only address emerging risks but also provide faculty with clarity, support, and flexibility.

 

For adult learners, data breaches may expose not only academic information but also sensitive personal and professional details. Strong data security practices such as encryption, access controls, regular audits, and incident response planning are essential to minimizing these risks. Institutions have an ethical responsibility to ensure that efficiency and innovation do not come at the expense of learner protection.

 

Power, Surveillance, and Learning Analytics

AI systems in education often operate through learning analytics, which track and analyze learner behavior to inform instructional decisions. While analytics can identify students who need support, they can also create surveillance environments that disproportionately affect adult learners who balance learning with work, caregiving, or health challenges (Prinsloo & Slade, 2017).

 

When predictive models label learners as “at risk,” those classifications may shape how instructors, advisors, or systems interact with them. Without careful governance, such systems risk reinforcing bias, reducing learner agency, and privileging efficiency over human judgment (Selwyn, 2019).

 

Empowering Adult Learners Through Digital Literacy

Supporting data privacy and security is not solely a technical challenge; it is also an educational one. Adult learners benefit from opportunities to develop digital and data literacy, including understanding privacy policies, consent mechanisms, and the implications of sharing data with AI systems (Selwyn, 2016).

 

Educators and institutions can empower learners by explaining how AI tools work in an accessible language, providing choices about tool use when possible, modeling ethical and transparent data practices, and encouraging critical reflection on technology’s role in learning. Such practices align with adult learning principles that emphasize autonomy, relevance, and respect for learners’ lived experiences (Knowles et al., 2015).

 

Toward Ethical and Trustworthy AI in Adult Learning

As AI becomes more prevalent in adult education, data privacy and security must be treated as foundational—not optional—components of effective learning design. Ethical AI systems prioritize learner rights, minimize data collection to what is necessary, protect data rigorously, and involve learners as informed participants rather than passive data sources (Floridi et al., 2018).

 

For adult learners, trust is central. When learners trust that their data are being handled responsibly, they are more likely to engage meaningfully with AI tools, experiment with new forms of learning, and fully benefit from technological innovation. Protecting data privacy and security is therefore not only a legal or technical obligation, but a pedagogical and ethical one.

 

References

Azevedo, L., Robles, P, Best, E. &and Mallinson, D. J. (2025). Institutional policies on artificial intelligence in higher education: Frameworks and best practices for faculty. New Directions for Adult and Continuing Education 2025, 188, 70–78. https://doi.org/10.1002/ace.70013

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Kasworm, C. E. (2010). Adult learners in a research university: Negotiating undergraduate student identity. The Journal of Continuing Higher Education, 58(2), 143–151. https://doi.org/10.1177/0741713609336110

Knowles, M. S., Holton, E. F., & Swanson, R. A. (2015). The adult learner (8th ed.). Routledge.

Means, B., Bakia, M., & Murphy, R. (2020). Learning online: What research tells us about whether, when and how. Routledge.

Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 46–55. https://doi.org/10.1145/3027385.3027406

Rose, A. D., Ross-Gordon, J. & Kasworm, C. E. (2023). Creating a place for adult learners in higher education: Challenges and opportunities. Routledge.

Selwyn, N. (2019). Education and technology: Key issues and debates (3rd ed.). Bloomsbury.

Selwyn, N. (2019). What’s the problem with learning analytics? Journal of Learning Analytics, 6(3), 11–19. https://doi.org/10.18608/jla.2019.63.3

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.