College Administrators

Table of Contents

Course Overview

The opening section introduces the purpose, philosophy, and outcomes of the course. It explains how Advanced AI Applications in Educational Leadership builds on foundational AI teaching concepts to prepare graduate students to manage AI systems for school improvement, data analysis, budgeting, and equity. It also outlines required technologies, grading components, and the capstone structure.

Week 1 – AI in Educational Leadership: Overview and Trends

Students begin by situating AI within the broader landscape of educational leadership. They explore frameworks for how AI supports administrative efficiency, decision-making, and strategic planning. The readings and discussion highlight both the promise and ethical challenges of AI at institutional scale.

Week 2 – Data Dashboards and Predictive Analytics

This module examines how system-level dashboards translate raw data into actionable insights. Students review research on K–12 dashboard adoption and learn to interpret indicators for continuous school improvement. A practical workshop has them analyze a sample district dashboard and propose decisions a leader could make from it.

Week 3 – Early Warning Systems for At-Risk Students

Students investigate machine-learning models that identify disengagement or dropout risk. Through research and simulation, they design an early-warning framework tailored to their own context, balancing data precision with student privacy and stigma concerns.

Week 4 – AI-Supported Decision Making for Interventions

This week connects prediction to action. Learners examine how AI can recommend interventions or tutoring strategies while maintaining human oversight. They practice designing tiered support responses to AI alerts, ensuring interventions remain individualized and evidence-based.

Week 5 – AI in School Operations

Attention shifts to the logistical side of leadership—how AI streamlines scheduling, staffing, and resource allocation. Students contrast human-made timetables with algorithmic solutions and evaluate trade-offs between efficiency and teacher preferences.

Week 6 – AI in Budgeting and Finance

This unit explores predictive models for budgeting, enrollment forecasting, and equitable funding formulas. Case studies show how states and districts use AI for financial planning, prompting students to debate transparency, bias, and accountability in AI-driven resource allocation.

Week 7 – Ethics and Equity in AI Deployment

Students delve into bias mitigation, privacy, and fairness. They apply human-rights and justice frameworks to school technology decisions, developing a checklist for ethical AI leadership and drafting responses to realistic ethical dilemmas.

Week 8 – Midterm Project Workshop

The midpoint focuses on developing and presenting a departmental AI intervention proposal. Students synthesize earlier concepts into a pilot plan addressing a concrete challenge (such as attendance, scheduling, or grading), then receive peer and instructor feedback to refine it.

Week 9 – Selecting and Evaluating AI Tools

Learners practice vetting AI products using research-based criteria for effectiveness, data security, bias, and accessibility. They simulate serving on a district procurement committee and create a rubric for responsible vendor selection.

Week 10 – Implementation and Professional Development

This week centers on change management. Students design training plans and communication strategies that build staff confidence in AI adoption, exploring how to align technology rollouts with district goals and teacher growth.

Week 11 – Policy and Governance

Students craft draft AI use policies and governance models for schools or districts. They learn how to formalize guidelines for acceptable use, data stewardship, and oversight, ensuring compliance with legal and ethical standards.

Week 12 – Case Studies and Future Directions

Through current examples from pioneering districts, students analyze how AI reshapes operations and culture. They map the ripple effects of future innovations and articulate strategies to remain adaptive, equitable, and forward-thinking leaders.

Week 13 – Capstone Presentations and Reflection

The course culminates in the presentation of full AI implementation or policy plans. Students demonstrate mastery by articulating rationale, ethics, and outcomes for their proposed initiatives, followed by collective reflection on lessons learned and next steps in responsible AI leadership.

Course Overview

This course introduces future educators to the principles, practices, and ethics of using artificial intelligence in K–12 teaching and learning.
Students learn how AI tools can enhance lesson design, differentiation, feedback, and accessibility — and how to use AI to teach skills directly: helping students strengthen reading comprehension, writing, problem-solving, and digital literacy.
Through guided labs, simulations, and reflection, participants develop fluency in AI-assisted pedagogy while maintaining a human-centered approach to instruction.
By semester’s end, they will design an AI-Integrated Unit Plan that demonstrates both technological proficiency and pedagogical soundness.

Technologies Used: ChatGPT, DALL·E, MagicSchool.ai, spreadsheet dashboards, speech-to-text and text-to-speech tools, and other free generative-AI platforms.
Major Assessments: Applied labs (50%), reflections and discussions (20%), and a final AI-Integrated Unit Plan (30%).

Week 1 – Teaching in the Age of AI

Students explore how AI is transforming the teaching profession.
They analyze how AI complements—not replaces—the teacher’s role in creativity, inquiry, and student care.
Discussion connects foundational education theories to emerging AI applications.

Week 2 – AI 101: Prompting for Educators

Learners practice crafting purposeful prompts to support instruction.
They study role-based and task-based prompting, model verification, and bias checking.
Lab: “Before and After” prompt rewrites and a Prompt Quality Checklist for classroom use.

Week 3 – Data-Driven Instruction with the WISSAM Model

Students use the WISSAM framework to connect assessment data to learning design.
They identify skill gaps and use AI to generate scaffolded lessons and formative checks.
Lab: Analyze a sample data report and generate a mini-lesson for targeted skill remediation.

Week 4 – Using AI to Teach Core Academic Skills

This week centers on how teachers can use AI to model and support skill acquisition in literacy, numeracy, and critical thinking.
Students experiment with AI-assisted reading comprehension tasks, writing prompts, and problem-solving routines.
They also explore AI as a “thinking partner” to promote metacognition and self-regulation in learners.
Lab: Design a short AI-supported reading or writing activity that explicitly teaches a core skill (e.g., inference, thesis writing, or problem-solving steps).

Week 5 – Personalizing Curriculum with AI

Students learn to differentiate lessons using AI to adapt reading levels, modalities, and examples.
They balance algorithmic suggestions with professional judgment to ensure cultural relevance and inclusion.
Lab: Simplify and enrich a single text, creating two leveled versions and corresponding activities.

Week 6 – Designing AI-Generated Assessments and Rubrics

Learners design formative and summative assessments using AI tools.
They evaluate validity, clarity, and alignment with standards.
Lab: Create a five-item quiz and rubric for a standards-aligned skill; annotate edits for fairness and precision.

Week 7 – Grading and Feedback with AI (Human in the Loop)

Students examine AI’s potential to assist in providing timely, personalized feedback while maintaining human oversight and empathy.
They discuss bias detection and ethical communication in AI-assisted grading.
Lab: Compare AI-generated versus teacher-written feedback; write a “Responsible Feedback Statement” for students.

Week 8 – Accessibility and Universal Design for Learning

Future teachers learn how AI can help remove barriers for learners with different needs.
They generate alt-text, captions, simplified readings, and multilingual supports.
Lab: Redesign a resource to meet UDL guidelines and produce a one-page Accessibility Plan for a lesson.

Week 9 – Creative and Multimodal AI for Student Engagement

This unit explores AI tools that enhance creativity and multimodal learning — including visual, audio, and code-based media.
Students discuss the role of AI in supporting student expression and digital composition.
Lab: Redesign a lesson element using AI-generated images, narration, or simulations to strengthen engagement and comprehension.

Week 10 – Academic Integrity and Responsible AI Use

Students learn how to teach ethical AI use to children and adolescents.
Topics include attribution, plagiarism, and explaining to students how AI can assist thinking without replacing it.
Lab: Draft a classroom-ready “Student AI Use and Integrity Policy.”

Week 11 – Ethics, Bias, and Equity in AI-Enhanced Classrooms

Learners analyze real examples of algorithmic bias and inequitable content generation.
They discuss fairness, representation, and data ethics through a classroom lens.
Lab: Create a Bias Awareness Checklist to guide responsible classroom AI use.

Week 12 – Teacher Reflection and Growth with AI

This week reimagines AI as a reflective tool for teachers.
Students use transcription or summarization tools to analyze lesson interactions and identify opportunities for improvement in questioning, pacing, and student talk.
Lab: Upload or analyze a teaching scenario; generate an AI summary of strengths and growth areas; write a reflection on the human insights AI cannot replace.

Week 13 – Capstone: Designing an AI-Integrated Unit Plan

In the culminating project, students design a standards-aligned unit that embeds AI across multiple stages of teaching and learning — from lesson planning and differentiation to feedback and skill development.
They demonstrate how AI will support both teacher efficiency and student mastery of academic and digital literacy skills.
Presentation: Showcase the AI-Integrated Unit Plan and reflect on how responsible AI use strengthens both teaching and student empowerment.

Learning Outcomes

By the end of this course, pre-service teachers will be able to:

1. Use AI tools to teach, model, and reinforce essential academic and cross-disciplinary skills such as reading comprehension, writing, and problem-solving.

2. Integrate AI thoughtfully into lesson planning, assessment, and feedback while maintaining pedagogical control.

3. Differentiate instruction for diverse learners using AI to adapt complexity, language, and modality.

4. Apply Universal Design for Learning and accessibility principles through AI-enabled content creation.

5. Demonstrate ethical AI literacy and guide students in transparent, responsible use.

6. Reflect on AI’s role in professional growth and lifelong teacher learning.

Week 1 – The AI Shift in Teacher and Leader Preparation

Participants explore how AI is influencing K–12 education — from classroom teaching and assessment to district operations and governance.
Discussions focus on how these changes redefine what teachers and administrators must know and how colleges of education can redesign programs to prepare them.

Week 2 – Mapping the Education Workforce in the Age of AI

This week provides an overview of how different roles in education are evolving.
Participants analyze how teachers, principals, instructional coaches, counselors, and district staff already use or are beginning to use AI in their work — from lesson planning and data analysis to communication and reflection.
Activity: Create a visual “AI Ecosystem Map” showing where graduates of different education programs will interact with AI tools.

Week 3 – AI in Teaching and Classroom Practice

Administrators examine what pre-service teachers now need to know about AI:
how to use it to design lessons, differentiate instruction, create assessments, support accessibility, and teach academic and critical-thinking skills.
Case studies highlight what “AI-literate teaching” looks like and how these competencies can be built into methods courses and student-teaching experiences.

Week 4 – AI for School and District Leadership

This module looks at how the principals, superintendents, and instructional leaders trained by colleges of education are using AI for planning and management.
Topics include dashboards, predictive analytics, scheduling, budgeting, and policy implementation.
Participants identify which leadership standards and courses should incorporate AI literacy, data ethics, and decision-making using AI insights.

Week 5 – AI for Counseling, Student Support, and Special Education

Leaders examine how school counselors, psychologists, and special educators are using AI for data tracking, documentation, and individualized support.
Readings highlight the advantages — improved efficiency, insight, and accessibility — as well as risks such as bias, privacy, and depersonalization.
Participants discuss how to prepare counseling and special education students to use AI responsibly in case management and IEP development.

Week 6 – AI and Higher Education Faculty Workflows

This week shifts the focus to the internal operations of the college itself.
Participants explore AI tools that can support faculty and staff productivity — from research synthesis and accreditation reports to advising and professional communication.
They discuss how to balance these benefits with concerns about academic integrity and plan faculty development approaches that promote ethical, confident AI use.

Week 7 – Curriculum Redesign for an AI-Integrated Future

Administrators conduct a curriculum audit to determine where AI competencies should appear across their programs — in teacher education, leadership, counseling, and general education coursework.
Workshop: Redesign one program sequence to embed AI-related learning outcomes, activities, and assessments while staying aligned with state and accreditation standards.

Week 8 – Faculty and Staff Development for AI Literacy

Participants design faculty and staff learning systems to build institutional AI capacity.
They create plans for professional development sessions, mentoring programs, and shared resource hubs that promote effective and ethical use of AI.
The course concludes with participants sharing strategies for fostering an informed, innovative culture that keeps human judgment and relationships at the center of education.