A detailed framework for
developing a Professional Learning Community (PLC) is provided through DuFour,
DuFour, and Eaker’s (2008) six key principles. The DuFour et al. model emphasizes shared
mission and vision, collective inquiry, collaborative teams, action
orientation, continuous improvement, and results orientation. By utilizing this PLC model, a teacher
preparation program can design a structured training program to promote a collective,
ethical, and pedagogically sound approach to Generative AI. Let’s consider the
following detailed framework for this endeavor, which was intentionally aligned
with DuFour et al.’s six key PLC principles.
Establishing a unified purpose
around ethical and responsible AI use in education requires a shared mission,
vision, and values. Suggested drafts for each were presented in the September 30
blogpost. To implement this step of the PLC
process, begin with a visioning workshop where faculty, teacher candidates, and
instructional technologists articulate how AI aligns with the university’s
mission and teacher education standards such as InTASC,
CAEP, or AAQEP. The outcome of this workshop
should be the creation of a shared statement of principles that specifies the integrity,
transparency, inclusivity, and learner agency for AI utilization in teaching,
learning, and assessment. It would be helpful for this workshop to anchor
discussions in the professional ethics of teaching, which includes, but is not limited
to, fairness, academic honesty, and equity in access.
Based on the second principle, the PLC should then seek to invite collective inquiry. Fostering systemic curiosity, reflection, and shared understanding about AI’s educational implications invites diverse faculty to investigate key questions such as:
· “How can Generative AI support UDL and differentiation?”
· “What are ethical red flags in AI-assisted lesson planning?”
Teacher preparation faculty
should be encouraged to incorporate action research whereby candidates test AI
tools in simulated teaching tasks such as lesson plan generation and formative
assessment feedback with the expectation to document outcomes and ethical
considerations. During this collective inquiry, intentionally invite faculty and
teacher candidates to regularly review emerging literature, policies, including
the ISTE AI standards, and examine case studies of ethical dilemmas.
Building
cross-disciplinary partnerships that model the collaborative spirit of the DuFour
et al. (2008) PLC model transitions the institution to the third principle of
implementation. Teacher preparation faculty can intentionally organize AI
learning teams, comprised of teacher educators, preservice teachers, and
technology specialists who co-design and critique AI-integrated teaching
modules. This provides an opportunity for encouraging peer modeling and
feedback. Visualize candidates presenting AI-supported lesson plans to
peers for both ethical review and pedagogical critique. These present6ations
should use shared digital spaces such as Microsoft Teams, Google
Workspace, Canvas
discussions to collaboratively document and archive guidelines and
exemplars.
The
fourth principle of PLC implementation should move beyond discussion toward
applied, iterative practice. This stage
of the PLC is action oriented and experimental. Teacher preparation and
other faculty are now implementing embedded AI-integrated microteaching
activities whereby candidates practice ethically using tools like ChatGPT, Diffit, or Canva
Magic Write under guided protocols. This encourages the teacher candidates
and others’ “Growth mindset” (Dweck, 2009) whereby a safe space is provided to “fail forward” through
formative assessment reflection cycles whereby intentional invitations
encourage analysis of what worked, what ethical concerns arose, and how to
revise practices. Also, respective of Web Content Accessibility
Guidelines (WCAG), developing “AI Sandbox” sessions allows teacher
candidates to test AI for accessibility, inclusivity, and bias detection.
The fifth PLC principle for implementation seeks continuous improvement by viewing ethical AI literacy as an evolving competency. To facilitate, the PLC can encourage reflection audits each semester to monitor how teacher candidate and other stakeholders’ competencies and attitudes toward AI have shifted. It is crucial to integrate ongoing professional development for faculty and cooperating teachers, so mentors in the field can consistently model program expectations. Maintaining a living document, regardless if called the AI ethics playbook or AI guide, allows the PLC and institutional process and policies to evolve as technology and best practices change.
The sixth principle for effective PLC implementation is results oriented, which requires reliable measurement of the impact of PLC-based AI training on ethical and instructional outcomes. Valid data requires reliable, measurable outcomes. For instance, consider what might be revealed by:
- Candidates demonstrating and documenting ethical AI use in lesson plans and reflection journals.
- Faculty integrating AI literacy into their course outcomes and assessment rubrics.
o Collecting
data through surveys, reflective essays, and digital artifacts designed to show
how candidates apply ethical principles.
o Using
data analysis to refine coursework, policies, and collaborative structures.
Collectively, the six principles
advocated by DuFour et al. (2009) should produce actual deliverables. Therefore, the following phases for a
suggested “AI in Education PLC Initiative,” Semester 1 Implementation Plan may
be helpful:
Phase 1:
Visioning and Principles Workshop (Weeks 1–2)
Phase 2:
Inquiry & Research Teams (Weeks 3–8)
Phase 3:
Application and Microteaching (Weeks 9–14)
Phase 4: Reflection, Assessment, and Policy Drafting (Weeks 15-16)
As previously provided, a structured
framework and a
professional development (PD) outline would help develop policies, create
professional development opportunities, and highlight classroom practices that
prioritize ethics, inquiry, and student empowerment. By applying DuFour et
al.’s (2008) PLC framework, the teacher preparation program shifts from
individual experimentation to collective ethical leadership in the use of
Generative AI. Candidates graduate not just as users of AI, but as reflective
practitioners who model responsible, transparent, and equity-centered digital
pedagogy
To cite:
Anderson, C.J. (October 31, 2025). Developing a collective
and ethical approach to generative AI approaches in teacher preparation. [Web
log post] Retrieved from http://www.ucan-cja.blogspot.com/
References:
Anderson, C.J. (September 30, 2025) Educational leaders: Promote ethical utilization of AI that encourages critical thinking skills! [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/
Dufour, R. (2006).
Learning by doing: A handbook for professional learning communities at work. Bloomington,
IN: Solution Tree Press.
DuFour, R., DuFour, R., & Eaker, R. (2008). Revisiting professional learning communities at work: New insights for improving schools. Bloomington, IN: Solution Tree Press.
International Society for Technology in Education.
(2025.). AI in education and accessibility. ISTE. https://www.iste.org
Purkey, W. W., & Novak, J. M. (2015). Fundamentals of
invitational education. (2nd Ed) International Alliance for Invitational
Education. Retrieved from: Fundamental
of Invitational Education | IAIE
Purkey, W.W., Novak,
J.M., & Fretz, J.R. (2020). Developing inviting schools: A beneficial
framework for teaching and leading. Teachers College Press.
U.S. Department of Education, Office of Educational
Technology. (2023). Artificial intelligence and the future of teaching
and learning: Insights and recommendations. https://files.eric.ed.gov/fulltext/
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