Higher education leaders today make consequential technology decisions that affect learning, administration, and institutional sustainability — often without sufficient preparation for the complexity those decisions involve. Selecting a learning management system, deciding how to respond to generative AI, investing in student success analytics, or migrating to cloud-based enterprise systems are not merely technical decisions; they are strategic, financial, pedagogical, and organizational choices with long-term consequences. ED7845 develops the technology leadership intelligence that higher education leaders need to make these decisions well.
Technology for teaching and learning
Understanding instructional technology from a leadership perspective
- Learning Management Systems and online learning infrastructure: ED7845 examines the dominant technology infrastructure of contemporary higher education — the LMS (Canvas, Blackboard, Moodle, D2L Brightspace) that mediates most course-related technology interactions. The course develops leadership understanding of LMS selection, implementation, governance, and evaluation; the relationship between the LMS and other instructional technology systems; and the pedagogical implications of LMS design choices (how platform affordances shape what instructors can easily do and what they cannot)
- Artificial intelligence and generative AI: The course examines the rapid emergence of AI tools in higher education — generative AI systems (ChatGPT, Claude, Gemini, Copilot) that can produce academic text, solve problems, write code, and generate creative artifacts; AI tutoring and adaptive learning systems; AI-powered feedback tools; and AI analytics applications. ED7845 develops the leadership capacity to evaluate these technologies critically (distinguishing genuine educational value from hype), develop institutional policies for appropriate use, and address the academic integrity implications of generative AI at scale
- Student success analytics: The course examines the growth of learning analytics and student success platforms (EAB Navigate, Civitas Learning, Brightpoint) that use institutional data to identify at-risk students, trigger proactive interventions, and support advisor caseload management. ED7845 develops critical analysis of these systems' evidence base, their equity implications (whether predictive models embed and amplify existing disparities), the data privacy and governance considerations of large-scale student data analysis, and the leadership infrastructure needed to act effectively on analytics insights
Technology for institutional management
ED7845 examines the technology systems that support higher education's administrative and operational functions. Enterprise Resource Planning systems (ERPs — Workday, Oracle PeopleSoft, Banner, Ellucian) that integrate financial management, human resources, student information, and other administrative functions across the institution represent some of the largest and most complex technology investments that institutions make — and some of the highest-risk, with numerous high-profile implementation failures that have disrupted institutional operations and cost institutions tens of millions of dollars. The course examines ERP selection, implementation governance, change management, and the organizational capabilities that determine implementation success or failure. Customer Relationship Management systems (CRMs — Salesforce, Slate, Ellucian CRM Recruit) have transformed enrollment management, with sophisticated communication and relationship management tools enabling more personalized and data-informed student recruitment and retention outreach. Financial and budget management technology, cybersecurity infrastructure, facilities management systems, and institutional research data warehouses round out the administrative technology portfolio that leaders must understand and govern effectively.
Technology adoption decision-making
ED7845 develops frameworks for making sound technology adoption decisions in higher education — decisions that are frequently made poorly, driven by vendor marketing, peer institution imitation, or trustee enthusiasm rather than systematic analysis of institutional needs, implementation requirements, and evidence of educational effectiveness. The course covers technology needs assessment for higher education (how to identify genuine technology gaps and distinguish them from technology desires driven by novelty or competition), technology evaluation frameworks (applying evidence standards to vendor claims, conducting pilots and evaluations, learning from peer institution experiences), total cost of ownership analysis (moving beyond license and implementation costs to include ongoing support, training, integration, and refresh costs over the technology's full lifecycle), change management for technology implementation (how to prepare faculty, staff, and students for technology changes — the most common cause of technology implementation failure is insufficient attention to the human and organizational dimensions of change), and vendor relationship management (negotiating contracts, managing vendor performance, planning for technology transitions and replacements).
Governance, equity, and ethics in technology leadership
ED7845 examines the governance structures and ethical frameworks that should guide technology decision-making in higher education. Technology governance (who makes technology decisions, how they are made, how different stakeholder perspectives are represented) is often underdeveloped in higher education — decisions made in silos by IT departments, academic units, or administrative offices without coordination or oversight lead to redundant systems, incompatible platforms, and misaligned investments. The course examines shared governance models for technology decision-making and the institutional committee structures that enable better decisions. Equity and access considerations are central: technology investments that inadvertently favor students with better devices, faster internet connections, or greater technology confidence widen the digital divide that institutions should be working to close. Cybersecurity and data privacy governance have become urgent institutional priorities as higher education institutions face increasing threats from ransomware, phishing, data breaches, and state-sponsored intrusions targeting research data. ED7845 develops the leadership literacy to engage with these governance challenges without pretending that senior leaders need to become technical experts.
ED7845 assignments include technology audits, adoption decision analyses, strategic plans, and equity impact assessments
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Technology audits, adoption analyses, strategic plans, equity impact assessments, AI policy frameworks.
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Frequently asked questions
AI governance has become one of the most pressing technology leadership challenges for higher education, and ED7845 addresses it with the seriousness it deserves. The rapid proliferation of generative AI tools (ChatGPT released in November 2022; widespread adoption across higher education within months) caught most institutions without governance frameworks, policies, or preparation, forcing reactive responses to a technology whose capabilities and implications were changing faster than institutional processes could follow. Effective AI governance in higher education involves several interconnected dimensions. Academic integrity policy: generative AI's capacity to produce academic writing, solve homework problems, and generate code fundamentally challenges traditional academic integrity frameworks designed for a world where such tools did not exist. Effective institutional responses involve developing nuanced policies (rather than blanket prohibitions that cannot be enforced) that distinguish between AI use that supports learning (brainstorming, feedback, accessibility support, draft review) and AI use that bypasses learning (generating submission content that represents no student work). These policies require faculty development to understand both the capabilities and limitations of current AI systems and the pedagogical adjustments needed to design assignments that retain educational value in an AI-available environment. Data privacy and security: AI tools that process student work, institutional data, or research data raise data governance questions about what data may be submitted to external AI systems, what agreements govern institutional data use by AI providers, and how to evaluate AI vendor data protection commitments. Student equity: AI tools may widen existing inequities if students with more technical sophistication, better devices, or more time exploit AI advantages unavailable to working students, first-generation students, or students with less technical exposure. Curriculum implications: if AI can perform tasks that courses currently assess, the course's learning objectives require examination — are students learning to do things that are now commoditized (produce a basic literature review, write a functional code snippet) when they should be learning higher-order skills (evaluate source quality, design algorithms, integrate and synthesize across complex literatures)? ED7845 develops the governance literacy to navigate these dimensions with the nuance they require.