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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/

Proficiency with Formative Assessment Strategies can Help Faculty Effectively Plan for the Students’ Ethical Utilization of Generative Artificial Intelligence

Planning for differentiated processes in coursework offers a powerful and practical entry point for institutionalizing formative assessment because both approaches share a common foundation: Responsiveness to student needs and a commitment to growth over time. Inherently, differentiated processes offer varied pathways for students to engage with content, develop skills, and demonstrate understanding. By embedding formative assessment into these processes faculty can gather ongoing, meaningful evidence about how students are progressing on different paths.  This can facilitate timely adjustments to teaching based on each student’s zone of proximal development (Vygotsky, 1978). This will reinforce a classroom culture that values learning as a process rather than solely as an outcome. By planning for differentiation, faculty are naturally positioned to build in checkpoints that support personalized learning through formative feedback.

Differentiated planning and processes typically include scaffolding, tiered tasks, and provision of choice, which also creates the opportunity to apply UDL 3.0 Guidelines and Considerations. Each element of differentiation and application of UDL Principles for Postsecondary Professional Development  logically invite formative check-ins through progress journals, draft submissions or peer feedback.  Thus, the opportunity for students to receive guidance tailored to their selected process or challenge level, reinforces a system whereby formative feedback is not just a side activity, but part of the course architecture. This results in formative assessment processes feeling integrated and essential rather than optional.

Differentiated planning acknowledges that students bring diverse experiences, skills, and needs to the classroom. Formative assessment complements this by providing students multiple low-stakes chances to show progress and receive support. This intentional opportunity encourages reflection, self-assessment, and student empowerment in the learning process.  This further helps faculty recognize when differentiation is either working or when it needs adjustment. Together, differentiated planning and formative assessment practices normalize variability and support inclusive teaching.

Proficiency with formative assessment strategies can be instrumental in helping university faculty guide students toward authentic and ethical use of generative artificial intelligence (AI). Formative assessments often include reflective activities.  Journals, drafts with feedback, or self-assessments can prompt students to think critically about how they are using AI tools, why they are using them, and whether their usage aligns with course expectations and academic integrity. This encourages transparency and ethical reflection in their AI utilization.

Faculty can provide opportunities for responsible AI utilization through low-stakes assessments or designing tasks that encourage exploration of AI capabilities.  By Integrating scaffolded discussions on bias, accuracy, originality, and ownership faculty can provide feedback on both content and process, including how students documented or integrated AI. These formative strategies create space for instruction and correction before summative grading.

By frequently using formative assessments through progress checks, drafts, or peer reviews, for example, faculty can monitor student thinking and development over time, identify discrepancies in tone, style, or reasoning that may suggest overreliance upon AI.  Therefore, faculty can then intervene early with feedback, support, or redirection. 

Proficiency with formative assessment strategies helps prevent academic dishonesty and guides students toward ethical habits. Faculty can embed clear expectations and transparent modeling of ethical AI utilization into formative tasks such as requiring students to annotate where and how AI tools were used, planning for reflective writing on the role of AI in their developmental and editing processes or co-constructing norms and policies around AI use with the students. The result is formative assessment approaches becoming a platform for building a shared understanding of acceptable practices.

Obviously, not all students will have the same familiarity or comfort with AI. Employing formative assessment strategies can help faculty identify students who need technical support or ethical guidance so faculty can effectively differentiate instruction accordingly.  Adjusting course design or support resources to meet evolving needs is simply good instruction that invites learners to optimize their human potential. This leads to more equitable, intentional, and ethical integration of generative AI. Yes, formative assessment strategies provide a feedback-rich, low-stakes environment that is ideal for fostering responsible, reflective, and ethical engagement with AI tools in higher education. 

Institutions aiming to scale formative assessment practices should consider adopting differentiation frameworks as UDL to anchor its course design.  Such an adoption can highlight how formative assessment fits seamlessly into differentiated planning and instructional practice.  It would be important to offer professional development that synchronizes differentiated planning and formative assessment practices as mutually reinforcing rather than as separate pedagogies. This could reduce resistance and show instructors how to embed assessment in ways that enhance rather than complicate course design.

Effective differentiated processes generate richer, more individualized data through formative assessment.  The synchronization makes it easier to track student growth, document how instructional flexibility improves outcomes, and build an institutional case for formative assessment as a system-level tool for student success. When coursework embeds differentiated processes, formative assessment becomes both a natural and necessary mechanism for monitoring varied learning paths, supporting diverse learners, and ensuring instruction is dynamic and equitable. Thus, alignment makes it much easier for institutions to normalize and sustain formative assessment practices at scale.

The following activities were designed with the assistance of ChatGPT (OpenAI, 2024), a generative AI tool.  The first example is a potential starting point for Higher Education faculty to use AI to effectively develop students’ ethical utilization of Generative Artificial Intelligence. The second example exhibits how a teacher preparation course requiring application of Universal Design for Learning (UDL) principles in lesson planning and instructional design can further develop students’ ethical utilization of AI.

Formative Activity: "AI in My Process" – Reflective Draft and Annotation

Objective: Help students practice using generative AI as a thinking partner while developing transparency and ethical awareness about its role in their academic work.

 

Part 1: Draft a Short Written Assignment

Students complete an early draft of a course-related assignment (e.g., an essay, research summary, discussion post).

 

They are encouraged—but not required—to use a generative AI tool (e.g., ChatGPT, Grammarly, Bard) as a support resource (e.g., for brainstorming, outlining, or revising).

 

They must document and annotate all AI use directly in the draft using comments or footnotes (e.g., "I used ChatGPT to generate three initial topic ideas" or "I asked the AI to rephrase this paragraph more concisely").

 

Part 2: Reflective Memo (300–500 words)

Students submit a short reflective memo addressing the following prompts:

 

Describe how you used (or chose not to use) AI in the drafting process.

 

What did the AI contribute, and how did you evaluate its reliability, originality, or usefulness?

 

How did you ensure that your work remains your own, both intellectually and ethically?

 

Would you do anything differently next time you use AI? Why or why not?

 

Part 3: Instructor Feedback (Formative)

The instructor reviews the draft and reflection, focusing on:

 

Alignment with course and institutional academic integrity policies,

 

The thoughtfulness and transparency of the student's process,

 

Feedback on how to improve both the work and the ethical integration of AI tools.

 

Why This Works:

Encourages meta-awareness and ethical decision-making.

 

Allows students to experiment without penalty.

 

Helps faculty spot patterns of misuse or misunderstanding early.

 

Builds a classroom culture of open dialogue about technology and responsibility.

 

Formative Activity: “Designing with UDL and AI” – Annotated Lesson Plan + Reflection

Course Context: Teacher preparation course focused on applying Universal Design for Learning (UDL) principles in lesson planning and instructional design.

 

Objective: Help teacher candidates ethically and authentically explore how generative AI can assist in developing inclusive lesson plans aligned with UDL principles, while reflecting on its ethical and pedagogical implications.

 

Part 1: Draft a UDL-Enhanced Lesson Plan

Students draft a short lesson plan (or a portion of one) targeting a specific content area and grade level. The plan must include:

 

At least one goal, one method, and one assessment strategy explicitly aligned with UDL principles (e.g., multiple means of engagement, representation, and action/expression).

 

Students are encouraged to use a generative AI tool (e.g., ChatGPT, Curipod, MagicSchool.ai) to assist with idea generation, scaffolding strategies, or accessible materials.

 

Annotation Instructions

Students must annotate the lesson plan draft using comments (or footnotes) that answer:

 

Where did you use AI?

 

What did the AI generate?

 

How did you adapt or evaluate the AI-generated content to align with UDL?

 

Why was this helpful—or not helpful?

 

✅ Example Annotation:

 

"Used ChatGPT to suggest ways to represent vocabulary visually; adapted one idea (image cards) and rejected another (animated GIFs) due to age-appropriateness."

 

Part 2: Reflective Memo (300–500 words)

Prompt students to reflect on:

 

How did using AI impact your thinking or design process in relation to UDL?

 

How did you ensure the lesson remains responsive to learner variability rather than 'one-size-fits-all'?

 

What ethical considerations did you keep in mind while using AI in an educational planning context?

 

What are the implications of AI-assisted planning for equity, accessibility, and teacher responsibility?

 

Part 3: Instructor Feedback

Provide low-stakes, formative feedback focused on:

 

Appropriate integration of UDL principles,

 

Ethical awareness of AI use in educational contexts,

 

Clarity of student reasoning in annotations and reflection.

 

Optional: Use a rubric that lightly scores on transparency, UDL alignment, and ethical reflection (not AI "accuracy").

 

✅ UDL Tie-In for Faculty

This activity itself models UDL by offering:

 

Multiple means of engagement: students explore real-world tools like AI.

 

Multiple means of action and expression: students show learning via plans, annotations, and reflection.

 

Multiple means of representation: encourages integrating varied learner needs through AI-assisted supports.

 

To cite:

Anderson, C.J. (April 30, 2025) Proficiency with formative assessment strategies can help faculty effectively develop  students’ ethical utilization of generative artificial intelligence. [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.

CAST (2024) UDL 3.0 Considerations

NWEA. (2016). 4 formative assessment practices that make a difference in classrooms. https://files.eric.ed.gov/fulltext/ED567811.pdf

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

Planar D and Moya S “The effectiveness of instructor personalized and formative feedback provided by instructor in an online setting: some unresolved issues” The Electronic Journal of e-Learning Volume 14 Issue 3 2016, (pp196-203). www.ejel.org

Pozas, M., Letzel, V. and Schneider, C. (2020). "Teachers and differentiated instruction: exploring differentiation practices to address student diversity." Journal of Research in Special Educational Needs, 20: 217-230.

Stanford Center for Teaching and Learning. (2025). Differentiated instruction. https://ctl.stanford.edu/differentiated-instruction

Turner, W.D., Solis, O.J., and Kincade, D.H. (2017). Differentiating instruction for large classes in higher education, International Journal of Teaching and Learning in Higher Education, 29(3), 490-500.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press