Developing a Collective and Ethical Approach to Generative AI Practices in Teacher Preparation

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/

Educational Leaders: Promote Ethical Utilization of AI That Encourages Critical Thinking Skills!

To promote ethical utilization of Generative AI that encourages critical thinking skills, educational leaders should take a proactive, strategic, and values-driven approach. Having a structured framework and a professional development (PD) outline would help in this regard to develop policies, create professional development opportunities, and highlight classroom practices that prioritize ethics, inquiry, and student empowerment. This month we will examine the question: How might educational leaders promote best policies, practices, and PD programs for generative AI implementation in schools?

Integrating AI into pedagogy with purpose requires learning to use  generative AI to model Socratic questioning, hypothesis generation, and multiple perspectives. The school culture needs to encourage utilization of AI as a “thinking partner" rather than a content generator. For example, leaders should expect lessons that plan for students to use AI to brainstorm arguments, critically evaluate the generated output, then revise them using further evidence. Therefore, encourage grade 6-12 lessons that plan for students’ interrogation of AI outputs by identifying for bias, assessing credibility, and comparing to human sources. 

Fostering AI literacy and critical digital skills requires providing explicit training for both teachers and students on how generative AI’s basic mechanisms include limitations and potential biases. Explicitly train AI consumers how to ask effective prompts and critically analyze results. The process needs to include teaching how to integrate media and data literacy to equip students with tools needed to verify and challenge AI-generated content.

Modeling ethical decision-making requires educational leaders to exemplify ethical AI use during administrative and instructional decision-making processes. Willingly share real-life case studies of ethical dilemmas in AI such as plagiarism, misinformation, or surveillance to inspire relevant classroom discussion. Empower students to develop codes of conduct for their own AI use.

Creating a culture of inquiry and reflection requires the promotion of project-based learning whereby students use AI in creative, responsible ways.  Think how vibrant a class could be if students were empowered to design AI tools that addressed a community problem.  Opportunities abound and our options are only limited by our imaginations and willingness to innovate.  Begin by embedding reflective practices.  Journaling about AI use and decision-making or peer discussions on how AI influenced their thinking or learning process begins developing this culture of inquiry and reflection.

Supporting ongoing professional development requires leaders offering sustained, scaffolded PD for teachers.  Scaffolded support begins with pedagogically sound uses of generative AI. Teachers need to become familiar with AI tools and platforms aligned with learning goals. Evaluating student work when AI may be involved in the generation of assessments suggests the need for discussions and professional development.  These needs and more invite creation of learning communities for educators to share best AI-practices and concerns.

Monitoring, evaluating, and adapting AI policies, practices, and processes, requires regularly assessing the impact of AI tools upon student learning and critical thinking development. Educational leaders need to be ready to adjust policies and practices based on quickly evolving insights, technologies, or challenges.  This invites the involvement of students to give feedback on how AI is impacting their learning and thinking processes.

As advocated by Invitational Education theory and practice (Purkey & Novak, 2015), educational leaders should exhibit I-CORT: an intentional, caring, optimistic, respectful and trustworthy mindset (Purkey, Novak, & Fretz, 2020; Anderson, 2021) to invite optimal realization of the principle, “AI should amplify human thinking, not replace it (Hoffman, 2025).” By inviting AI implementation as an opportunity to enhance human reasoning, rather than replacing it, educational leaders can ensure that generative AI becomes a tool for empowerment rather than dependency.  As noted above a policy framework and a PD outline should help educational leaders implement generative AI ethically while fostering critical thinking skills in students.

To begin, educational leaders should draft a policy framework for ethical and critical AI use in schools.  This framework typically would entail a vision statement, guiding principles, and acceptable compared to unacceptable utilization scenarios.  The following are examples for each and invite further collaboration before implementing.

A Draft Vision Statement: "We believe generative AI should be used as a tool to empower learners, encourage critical thinking, and promote ethical decision-making. AI will be implemented in ways that support creativity, inquiry, and responsible digital citizenship."

Draft Guiding Principles:

·         Advance Human-Centered Learning: AI supports, but does not replace, human judgment, inquiry, and originality.

·         Promote Ethical Responsibility: All AI use must respect privacy, equity, fairness, and integrity.

·         Exhibit Transparency & Consent: Stakeholders, including students, parents, educators will be informed of how AI is used in learning and data handling.

·         Develop AI Literacy for All: Students and staff will receive age-appropriate training on AI’s strengths, limitations, and ethical implications.

·         Optimize Critical Engagement: Students will be encouraged to challenge, verify, and contextualize AI-generated information.

Draft Acceptable Use Guidelines

Stakeholder	Example of Acceptable Use	Example of Unacceptable Use
Students	Using AI to brainstorm essay topics, then researching and writing the essay independently.	Submitting AI-generated text as original work without attribution.
Teachers	Using AI to generate example problems or differentiated materials.	Using AI to assess student work without human review.
Administrators	Leveraging AI for data analysis to inform instruction.	Using AI tools to monitor students without transparency or consent.

 

Effective educational leaders understand that a goal without a plan is just a wish.  Therefore, the following steps could be useful.  By its very nature, any implementation strategy is a starting point.

·         Plan your pilot programs: Launch AI use in select classrooms with diverse student populations.

·         Create a student AI use agreement: All students sign a Responsible AI Use Agreement.

·         Identify a review committee: Form an AI Oversight Committee that includes educators, students, parents, and information technologists to monitor AI use and guide adjustments.

Monitor, evaluate, and seek feedback throughout the pilot program.  Subsequently annual surveys for students and staff on AI’s impact on learning and engagement will sustain this practice. By reviewing incidents of misuse and addressing them through restorative, educational interventions, a growth mindset is instilled and high expectations maintained. Annual policy updates based on new research, technologies, and school needs help to make better possible.

Likewise, a clear goal and an action plan is needed for effective professional development (PD) when we desire to empower educators toward ethical AI integration.  Consider the worth of the following PD Goal:

·         To equip educators with the tools, mindset, and strategies to integrate generative AI in ways that enhance student inquiry and critical thinking while upholding ethical standards.

If seen as worthwhile, the following structure for 5 PD sessions may serve as a draft action plan for K-12 teachers, curriculum designers, instructional coaches. Note the importance of ensuring your PD sessions’ desired outcomes are observable and thereby measurable:

During session 1: Understanding Generative AI. The measurable objective could be, “Given Interactive demo of generative AI tools, participants will learn how AI tools like ChatGPT, DALL·E, and others work by categorizing the capabilities and limitations of generative AI.”

The debrief would include the participants discussing “What AI can and can’t do.”

During session 2: Ethical Considerations & Student Integrity. The measurable objective could be, “Given case study analysis eliciting, "What would you do?" participants will discuss ethical risks including plagiarism, bias, and surveillance identify (x) strategies for fostering academic integrity.”

The summary activity would include the participants drafting classroom AI use norms with colleagues.

During session 3: Designing AI-Enhanced Critical Thinking Tasks. The measurable objective could be, “Given sample lesson plans, participants will practice prompting AI for effective classroom use and embed AI into a lesson such as to enhance rather than replace student thinking.”

The summary activity could include the participant groups workshopping to rewrite one lesson plan to include AI as a tool for inquiry followed by cross peer-group feedback sessions.

During session 4: Assessing AI-Influenced Work. The measurable objective could be, “Given opportunities to review student work samples, participants will detect and assess AI-assisted student work and promote student reflective practices around AI use.

The summary activity could include the participants developing a (grade-level) reflection rubric based on the prompt: “How did you use AI, and how did it help or hinder your thinking?”

During session 5: Ongoing Learning & Leadership. The measurable objective could be, “Given a planned session, participants will exhibit skills of an AI leader or mentor in the school by collaboratively creating a grade or department AI use plan.

The summary activity could include the participants beginning an implementation plan for developing a collaborative AI integration guide or forming a professional learning community (PLC) for continued support.

The effective, intentionally inviting educational leader’s desire to establish clear ethical guidelines requires development of AI-use policies rooted in transparency, privacy, bias mitigation, and accountability. For this purpose, it is crucial to involve students, educators, and community stakeholders in crafting these guidelines to ensure broad support and awareness. The suggestions and guidelines provided above are provided as an opportunity to make it explicit that AI should support learning, not replace original thinking.

 

To cite:

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/

 

References:

Anderson, C. J. (2021). Developing your students' emotional intelligence and philosophical  perspective begins with I-CORT. Journal of Invitational Theory and Practice, 27, 36-50.

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/

Empowering New Teachers and Candidates: You Are Intentionally Invited.

 

New teachers and teacher candidates are essential for sustaining a school’s or institutional culture. The International Alliance of Invitational Education (IAIE) needs to recruit new teacher candidates to sustain and further promote its advocacy for an intentional, caring, optimistic, respectful, and trustworthy (I-CORT) school culture (Purkey & Novak, 2015; Anderson, 2020).  IAIE’s core mission is to foster positive and inclusive school cultures grounded in I-CORT principles. As current educators retire or move on, new teacher candidates are essential to carry the mission forward. Without intentional recruitment and empowerment of these individuals, the movement risks losing momentum and coherence over time.

Embedding I-CORT into every early teaching professional’s identity is a win/win focus point.  New teachers are at a formative stage in their professional journey. Introducing I-CORT values during their preparation helps to shape their foundational beliefs about teaching and learning, encourages them to see schools as places that should affirm every student’s worth, and promotes consistent practice aligned with invitational education principles. Therefore, when these values are integrated early, they become a natural part of a teacher’s pedagogical approach.

New teachers need to be part of creating a sustainable and scalable positive school culture.  A positive school culture isn’t maintained by a few individuals.  Rather, it requires a collective and systemic effort. Recruiting teacher candidates committed to I-CORT principles invites a critical mass of educators who consistently model caring and respect, work collaboratively with colleagues and students, and challenge negative or exclusionary practices. It is important that exhibited consistency is embraced as essential for sustainable cultural change at the school and district levels.

Innovation and fresh perspectives presented by new teacher candidates “makes better possible.”  New teacher candidates often bring energy, openness to learning, and new ideas. Their enthusiasm reinforces optimism within the school environment, can help adapt I-CORT values to modern educational challenges, and stimulate innovation in how I-CORT is practiced and promoted.

To advance this vision, it is essential to widen the reach and influence of the IAIE.  Recruiting new teachers can expand IAIE’s network and influence. As these educators implement I-CORT principles in diverse settings, they can act as ambassadors for invitational education, spread awareness to other educators, parents, and communities, and help the movement grow beyond its existing boundaries.

By addressing current educational challenges, the IAIE will continue helping today’s schools face complex social, emotional, and academic challenges. Teachers grounded in I-CORT values are more likely to advocate for building inclusive classrooms, fostering student engagement and trust, and promoting well-being and resilience.  Thus, the initiative to recruit and develop new I-CART-minded educators is vital to meeting the evolving needs of students and school communities.

Thus, recruiting new teacher candidates is essential for the IAIE to maintain and expand its vision of invitational education. These individuals are not just future educators, they are future leaders, culture builders, and advocates for schools that prioritize care, respect, and optimism. By empowering new teachers, IAIE secures both the present vitality and future relevance of the I-CORT philosophy.

The IAIE provides forward-thinking individuals, an affordable path to learning, implementing and helping us to improve Invitational Education theory and practice. Joining helps us work together to make your organization and the world a more welcoming place!

Advocates of Invitational Education are dedicated to encouraging the next generation to become involved with Invitational Education theory and practice. Joining us empowers you to make your schools and your future workplaces more productive, enjoyable and inviting! Collaborating with an intentional, caring, optimistic, respectful, and trustworthy (I-CORT) mindset can make the world a better place!

To get started, access the following IAIE membership link.

Additionally, your organization will constantly evolve, ideally seeking to improve. In this endeavor, the IAIE provides resources. You are invited to help bring Invitational Education to your school or professional organization. Conscientious educators and professionals can help promote Invitational Education in the interaction between the 5Ps: people, places, policies, programs, and processes of any school setting or institution for everyone's benefit. The IAIE wants to help you create a welcoming environment where all individuals flourish and bring their best ideas and most creative work into play. Making the world a better place is no small task.  However, together, we can make better possible! 
An institutional membership will help your organization create a blueprint for continued success and will qualify your institution to apply for IAIE awards. 

• An Institutional Member is $175.   It includes up to 5 memberships for people in your institution.

Please, join us.  Together we will be blessed by the opportunity to trudge a path that still needs to be cleared in many ways.  Becoming more familiar with IE theory and practices will empower your utilization of all the resources within your growing pedagogical toolbox.  

To Cite:

Anderson, C.J. (August 31, 2025). Empowering new teachers and candidates: You are intentionally invited. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/

 

References:

Anderson, C. J. (2021). Developing your students' emotional intelligence and philosophical  perspective begins with I-CORT. Journal of Invitational Theory and Practice, 27, 36-50.

 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

AI can Help to Improve Special Education Policies and Practices to Improve IDEA Compliance

Generative AI has significant potential to improve special education policies and practices that can enhance compliance with the Individuals with Disabilities Education Act (IDEA). Generative AI provides strengths in pattern recognition, personalization, and natural language generation.  This will help schools, educators, and policymakers address both procedural and substantive compliance more effectively. 

How can AI help with quality Individualized Education Program (IEP) development?  AI tools can assist educators in writing individualized, legally sound IEPs aligned with IDEA requirements by efficiently generating effective goal suggestions, accommodation ideas, and transition plans based on student profiles. AI can flag vague, incomplete, or non-compliant language in IEPs and suggest revisions based on legal criteria and best practices. Tools can evaluate goals for being Specific, Measurable, Achievable, Relevant, and Time-bound (SMART), which is a common compliance expectation area under IDEA. Currently, AI-powered IEP writers include those in early-stage EdTech platforms or integrations with district SIS systems.

How can AI help support data-driven decision making?  AI can analyze academic and behavioral data from multiple sources, such as learning management systems (LMS), student information systems (SIS), and formative assessments to track student progress toward IEP goals and alert teams when students are not making expected gains. Predictive Analytics can help identify students at risk of not meeting goals or requiring re-evaluation, ensuring timely interventions and reevaluations as required under IDEA.

How can AI help improve procedural compliance and documentation? AI-enabled systems can monitor IDEA timelines such as evaluation within 60 days, annual IEP reviews, etc. and notify stakeholders of upcoming deadlines. Natural language models can review documents for compliance with procedural safeguards, consent forms, parent notices, and disciplinary documentation. This has the potential to reduce procedural errors that often trigger due process complaints or state complaints.

How can AI help personalize professional development for educators? AI can analyze teacher performance, student data, and compliance gaps to generate targeted professional learning modules. Through simulated scenarios, AI can create interactive IEP meeting simulations or case studies for staff training aligned with IDEA and ethical standards.

How can AI help increase family engagement and transparency? Using plain language translation allows AI to translate IEPs and procedural documents into family-friendly language or other languages to support English Learners’ families, increasing access and participation. Chatbots or assistants can help families understand their rights, timelines, and IEP processes anytime, reducing misunderstandings and mistrust.

How can AI help inform policy reform through large-scale analysis? Through policy text analysis, generative AI can synthesize and compare local, state, and federal special education policies to identify inconsistencies, gaps, or outdated practices. To identify trends, natural language processes (NLP) can be used to analyze due process decisions, complaint data, and monitoring reports to uncover systemic compliance issues or patterns in service delivery.

How can AI help support inclusion and UDL-aligned instruction? To promote content adaptation, AI can generate accessible instructional materials, including text simplification, image-supported text, and audio versions to support students with diverse needs being served under IDEA.

Through the development of personalized learning paths, generative AI can align differentiated tasks with IEP goals, ensuring instructional access while maintaining rigor and alignment with grade-level standards.

So, what are essential implementation considerations? To align AI use with IDEA's intent and ethical standards, systems must ensure:

• Human-in-the-Loop Oversight , whereby AI assists but does not replace professional educators and clinicians.
• Data Privacy and FERPA Compliance
• Bias Detection and Fairness Audits
• Transparency in AI Decision-Making

Yes, generative AI can play a transformative role in strengthening IDEA compliance. This endeavor can only be enhanced by addressing the need for accuracy, timeliness, and personalization in special education. When thoughtfully implemented, AI can empower educators, support families, and safeguard the rights of students with disabilities, thereby advancing both the letter and spirit of IDEA.

  

 To cite:

Anderson, C.J. (July 31, 2025) AI can help to improve special education policies and practices to improve IDEA compliance. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/

 

References:

International Society for Technology in Education. (2025.). AI in education and accessibility. ISTE. https://www.iste.org

Limon I. (2022). Relationship between empowering leadership and teachers’ job performance: Organizational commitment as mediator. Journal of Theoretical Educational Science, 15(1), 16–41. https://doi.org/10.30831/akukeg.945201

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

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/

Invitational Education Theory and Assumptions can Guide AI Utilization for Effective Teaching and Learning

 As developed and advocated by Purkey and Novak (2015), Invitational Education (IE) theory and practices, emphasize empowering people to create places, policies, programs, and process that invite all students to realize their potential. IE is grounded in five guiding principles: intentionality, care, optimism, respect, and trust (I-CORT). IE’s core values can offer a human-centered, ethical lens for integrating generative artificial intelligence (AI) into effective teaching and learning.

 Intentionality should be the foundation of an I-CORT mindset (Purkey & Novak, 2015; Purkey, Novak, & Fretz, 2020; Anderson, 2024). Positive outcomes come from intentional actions. This assumption empowers people to thoughtfully integrate AI to support meaningful learning. When people intentionally and strategically use AI implications include enhancing student engagement, agency, and access rather than becoming a replacement for teaching. For example, when AI is accessed in a Universal Design for Learning (UDL)-aligned platform, teachers can more readily offer multiple means of representation and expression aligned to their knowledge of the students’ diverse learning needs or preferences.

 Perhaps a paradox, but stakeholders can promote care by promoting human connection through AI-enhanced teaching.  Advocates for IE theory and practices believe caring relationships are at the heart of effective education.  Therefore, AI should augment human interaction rather than reduce it. AI can efficiently design teaching and learning tools that free teachers to focus more on relationships. When AI is utilized to automate routine tasks such as basic grading, teachers can dedicate more time to mentoring students through formative processes.

 Surely optimism will be more evident when teachers use AI to amplify students’ potential rather than their deficits. Proponents of IE believe every student has untapped potential. A teacher’s unconscious biases can be mitigated through AI utilization that avoids deficit-based labels and instead, reveals strengths and growth areas. The adaptive assessment tools available through AI can efficiently highlight progress and recommended enrichment opportunities that the teacher effectively identifies based on knowledge of the student.

 IE theory believes that every person is valuable and capable of learning.  This respect for the learner would be exhibited by the teacher designing or identifying AI tools that honor student dignity and identity.  Therefore, ensure AI tools respect diverse identities by monitoring these tools for culturally responsive design and unbiased algorithms. For instance, only use AI-powered writing tools that offer constructive, formative feedback without penalizing dialects for multilingual learners.

 Completing the I-CORT mnemonic, trust is developed and sustained only by building or demanding transparent and ethical AI systems. Every learning environment’s 5 Ps: its people, places, policies, programs, and processes, are essential elements that need to be trustworthy to foster student growth. So, educational leaders and developers must prioritize data privacy, transparency, and explainability in AI systems. This is exemplified when using AI platforms that allow students and teachers to understand how data is used and how recommendations such as personalized learning paths are developed.

 By aligning generative AI utilization with the guiding principles of Invitational Education theory, educators can ensure that technology serves as an invitation to learn and grow. Otherwise, we can quickly become complicit in managing a surveillance mechanism or gatekeeping instrument. Invitational Education theory, practices, and assumptions create inclusive, empowering, and ethical learning environments whereby empowered teachers help students feel valued, supported, and challenged.

  

To cite:

Anderson, C.J. (June 30, 2025) Invitational Education theory and assumptions can guide AI utilization for effective teaching and learning. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/

 

References:

Anderson, C. J. (2021). Developing your students' emotional intelligence and philosophical perspective begins with I-CORT. Journal of Invitational Theory and Practice, 27, 36-50.

Black, P., & Wiliam, D. (2006). Developing a theory of formative assessment. In J. Gardner (Ed.), Assessment and learning (pp. 81–100). Sage.

Calisici Celik N., Kiral B. (2022). Teacher empowerment strategies: Reasons for non fulfilment and solution suggestions. Journal of Qualitative Research in Education, 29, 179–202. https://doi.org/10.14689/enad.29.7

CAST (2024). Universal Design for Learning Guidelines version 3.0. Retrieved from https://udlguidelines.cast.org

International Society for Technology in Education. (2025.). AI in education and accessibility. ISTE. https://www.iste.org

Limon I. (2022). Relationship between empowering leadership and teachers’ job performance: Organizational commitment as mediator. Journal of Theoretical Educational Science, 15(1), 16–41. https://doi.org/10.30831/akukeg.945201

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. 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/

The Impact of AI Upon MTSS Processes and Policies

Generative AI-driven educational software is going to become more, not less, pervasive. So, teachers need to be highly trained in effective implementation of evolving teaching and learning applications. Therefore, the science of teaching and learning-effective pedagogy- involves more than telling a student to access an application. Teacher preparation programs and institutional policies must lead this professional development endeavor.

Effective multi-tiered systems of support (MTSS) should ensure that differentiated strategies and formative assessment processes are embraced as two sides of the metaphorical coin we call teaching and learning. When both are planned and implemented well, differentiated approaches are part of the leading indicators and then formative assessment processes are the trailing indicators of effective, inclusive success. A pedagogical toolbox requires up-to-date professional soft skills rather than more classroom hardware that promises access to equity through software packages.

Generative AI has the potential to significantly transform multi-tiered system of support (MTSS) processes and policies, especially in the context of educating diverse learners. AI can positively impact MTSS processes through reliable data analysis and effective early identification. Generative AI provides the potential to analyze large, complex datasets such as academic records, behavioral logs, attendance tracking to identify patterns and early warning signs related to student risk factors.  For instance, AI can flag learners who may need Tier 2 or Tier 3 interventions based on writing patterns and student engagement with learning materials.

Generative AI can provide personalized intervention programs. AI can generate customized reading passages, scaffolded assignments, or language supports that are aligned to a student’s language proficiency, reading level, or cultural context. This is especially useful for English language learners (ELLs), students with disabilities, and others with individualized needs.

Generative AI has the potential to collect and analyze progress monitoring data.  Automation creates the opportunity for frequent, adaptive formative assessments and real-time feedback reports. AI-powered application dashboards can dynamically adjust goals and recommend instructional next steps based on student performance.

In this way, generative AI provides real-time teacher support and aid in data-based decision-making. AI can provide suggested interventions and evidence-based practices for specific learner profiles, reducing cognitive load on educators. However, highly-trained teachers must ensure that these recommendations are transparent, bias-sensitive, and evidence-aligned.

Soon generative AI will impact policy related to equity and bias considerations. AI tools may unintentionally reinforce systemic biases, especially if algorithms are developed or teachers are trained on biased datasets that over-identify certain groups for Tier 3. To mitigate this, audits for algorithmic fairness is essential, especially when used with diverse learners related to race, language, or disability.

Generative AI will also soon be utilized to revise data privacy and consent considerations. Updating FERPA, IDEA, and state-specific policies to govern AI use in student data processing is logical. This is especially true for policies involving minors and vulnerable populations.

Being effective with people and efficient with time is just good leadership (Covey, 1989). Generative AI can identify staff training schedules and professional development needs.  Effective MTSS frameworks must include PD for educators on using AI ethically and effectively.  This crucial aspect for AI implementation will ensure more effective human oversight and culturally responsive practices.

Generative AI will help re-define what entails evidence-based interventions. A current challenge is AI-generated interventions might not meet current federal or state definitions of “evidence-based.” Therefore, an immediate policy implication is the need to establish criteria for evaluating AI-assisted practices within MTSS.

Certainly, Generative AI has implications for diverse and English Language Learners (ELLs). In relation to students with disabilities, AI can co-create accessible materials such as text-to-speech or customized visual supports. However, the highly trained teacher must ensure anything generated by AI aligns with IEPs and legal mandates under IDEA.

While AI can dynamically translate and simplify texts, the highly trained teacher needs to ensure culturally relevant examples. While AI can dynamically translate and simplify texts, the highly trained teacher needs to ensure culturally relevant examples. For culturally and linguistically diverse (CLD) students, AI will be able to help personalize learning experiences that honor students' backgrounds. However, there is always a risk of reinforcing stereotypes if generative models are not curated or culturally aware. It must be the teacher rather than AI who ensures culturally responsible practices by ensuring fidelity of language support and maintaining human validation to avoid linguistic misrepresentations.

Given potential risks involved with AI implementation, especially related to developing processes and policies, districts and schools should pilot AI in MTSS within controlled environments with teacher oversight. Planning for AI implementation must seek to establish ethics and equity audits to ensure AI-driven tools do not exacerbate disparities. Begin by developing interdisciplinary teams that involve educators, data scientists, families, and advocates in AI-based tool development and evaluation.

Avoid being efficient with people. Mandating transparency begins with district leaders and its teachers understanding how AI makes decisions and being proficient when explaining it to families and students. Otherwise, teachers are more likely to be led by AI algorithms rather than empowering their effective teaching and educational leadership.

 

 To cite:

Anderson, C.J. (May 31, 2025) The impact of AI upon MTSS processes and policies. [Web log post] Retrieved from http://www.ucan-cja.blogspot.com/

 

References:

Black, P., & Wiliam, D. (2006). Developing a theory of formative assessment. In J. Gardner (Ed.), Assessment and learning (pp. 81–100). Sage.

Covey, S. R. (1999). The 7 habits of highly effective people: Powerful lessons in personal change. Free Press

International Society for Technology in Education. (2025.). AI in education and accessibility. ISTE. https://www.iste.org

OpenAI. (2024). ChatGPT (GPT-4 model) [Large language model]. https://chat.openai.com

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/