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Crafting a Sustainable Future: A Long-Term Strategy and Roadmap for Integrating Generative AI in Higher Education

What is you campus’ vision of Generative AI (Gen AI)? Is your campus leadership still on the fence or reluctant to embrace this transformative technology?

Gen AI is likely to remain a key element in higher education, but currently, the sector’s response is often compartmentalized, reactive, and short-term, focusing mainly on immediate risks rather than embracing the technology as a long-term, revolutionary asset.

This perspective is based on the following observations:

A long-term and sustainable strategy requires key strategic pillars and a roadmap. Here are some ideas to consider.

Key Strategic Pillars:

Strategic Leadership and Governance

  • Governance Framework: Establish a committee with administration, faculty, staff, and student representatives to oversee AI policy and strategy.
  • Ethical Use Policy: Create a campus-wide code of ethics for responsible AI use.
  • Long-Term Vision and Strategy: Articulate a vision that aligns AI with the institution’s long-term goals, involving all campus community members.

Ethics, Governance, and Public Policy

  • AI Governance: Establish structures for AI governance, focusing on ethical considerations and inclusive representation from all campus groups.

Curriculum Development and Academic Enhancement

  • Course Development: Integrate AI topics across curricula for students and training programs for staff.
  • Workshops and Certifications: Offer development workshops and certification programs in AI applications and ethics for faculty, staff, and students.
  • Curriculum Integration and Evolution: Evolve curriculum to include AI in diverse subjects, creating AI-focused degrees and staff development opportunities.

Continuous Learning and Development

  • Development Programs: Offer ongoing learning opportunities in AI for students, faculty, and staff, including mentorship and leadership programs.

Research and Innovation

  • Research Agenda: Identify and pursue key AI research areas, leveraging strengths across faculty, staff, and students.
  • Startup Incubator: Support AI startups from the campus community, providing resources and support.
  • Collaborative Research and Development: Encourage collaborative research involving faculty, staff, and external partners.

Resource Allocation and Infrastructure

  • Faculty and Staff Development Funds: Allocate grants for AI research and pedagogical innovation, including staff training programs.
  • Infrastructure Investment: Upgrade facilities for AI, ensuring access for research and practical applications by all campus members.
  • Operational Guidelines and Training: Implement AI guidelines for administrative tasks, providing training for all relevant staff.

Community Engagement and Global Collaboration

  • Knowledge Exchange Network: Establish a platform (e.g. Community of Practice) for AI knowledge sharing among faculty, staff, students, and the broader community.
  • Public Seminar Series: Host seminars on AI’s impact, encouraging participation from the entire campus and public.

Career Development and Networking

  • Career Network: Build an AI career network offering guidance, job placement services, and mentorship, connecting students, staff, and alumni in the field.

Monitoring, Evaluation, and Sustainability

  • Annual AI Symposium: Host an event to showcase AI developments, inviting external experts and encouraging participation from all campus members.
  • Strategy Review: Regularly assess and update the AI strategy based on feedback from faculty, staff, students, and external developments.
  • Sustainability and Adaptability: Focus on sustainable and adaptable practices in AI ecosystem development, involving all campus sectors.

Alumni and Industry Engagement

  • Alumni Involvement: Engage alumni in AI initiatives, facilitating connections with current students, faculty, and staff.
  • Industry Partnerships: Develop partnerships for internships, research, and application insights, involving the entire campus community.

Outreach and Public Engagement

  • Community Outreach: Implement AI literacy programs for the community, involving staff and students in outreach activities.
  • Public Engagement Activities: Organize activities to promote public understanding of AI, encouraging participation from all campus members.

Future-Oriented Analysis and Scenario Planning

  • Predictive Analysis and Scenarios: Use analytics to anticipate AI trends, considering their implications for education and campus operations.

Financial Strategy and Sustainability

  • Funding Strategies: Develop plans for securing funding and ensuring ROI on AI investments, considering the needs of the entire campus.


Phase 1: Foundation and Awareness

Duration: 6-12 months

  1. Assessment and Awareness Building
    • Conduct an institutional audit to assess current AI capabilities and needs.
    • Initiate awareness campaigns about Gen AI for all campus members.
  2. Establishment of Leadership and Governance
    • Form a cross-functional Gen AI Steering Committee.
    • Define roles and responsibilities for AI governance.
  3. Initial Policy and Ethical Framework Development
    • Develop a preliminary Gen AI ethical use policy.
    • Start discussions on Gen AI implications in education and research.

Phase 2: Strategy Development and Planning

Duration: 12-18 months

  1. Strategic Planning and Vision Setting
    • Develop a clear, long-term Gen AI strategy aligned with institutional goals.
    • Set measurable objectives and key performance indicators (KPIs).
  2. Curriculum and Program Planning
    • Plan for the integration of Gen AI topics into existing curricula.
    • Design professional development programs for faculty and staff.
  3. Resource Allocation and Infrastructure Planning
    • Budget for Gen AI initiatives, including research, training, and infrastructure.
    • Plan infrastructure upgrades for AI support.

Phase 3: Implementation and Pilot Programs

Duration: 18-24 months

  1. Curriculum and Program Implementation
    • Introduce Gen AI topics into selected courses.
    • Launch training workshops and certification programs for staff and faculty.
  2. Pilot Research and Development Projects
    • Initiate pilot Gen AI research projects.
    • Establish a startup incubator for Gen AI innovations.
  3. Operational Integration
    • Implement Gen AI in administrative tasks as pilot programs.
    • Start upgrading infrastructure to support Gen AI applications.

Phase 4: Community Engagement and Expansion

Duration: 24-36 months

  1. Community Engagement and Knowledge Exchange
    • Organize public seminars and workshops on Gen AI.
    • Establish a Gen AI Community of Practice.
  2. Expansion of Curriculum and Research Programs
    • Broaden the integration of Gen AI across multiple disciplines.
    • Expand research projects and collaborations, both domestically and internationally.
  3. Operational Scaling and Further Integration
    • Scale up successful Gen AI applications in administration.
    • Continue infrastructure enhancements for wider Gen AI deployment.

Phase 5: Evaluation, Monitoring, and Continuous Improvement

Duration: Ongoing

  1. Regular Assessment and Strategy Review
    • Conduct biannual reviews of Gen AI initiatives.
    • Adjust strategies based on feedback and technological advancements.
  2. Continuous Professional Development
    • Provide ongoing learning opportunities in Gen AI for all campus members.
    • Keep curriculum and programs up to date with the latest AI advancements.
  3. Sustainability and Future Planning
    • Focus on sustainable practices in AI use.
    • Anticipate future AI trends and prepare the institution accordingly.


The Ethical and Responsible Use of AI in Higher Education

What is the ethical and responsible use of AI in higher education?

I came across this concept of FATE: Fairness, Accountability, Transparency, and Ethics from Microsoft and other organizations. The question above, my curiosity about ethics frameworks, and the FATE concept led me to think more about the ethical and responsible use of AI in higher education.

The appropriate use of Generative AI like ChatGPT in the classroom dominates discussions on social media. But, applied to all facets within higher education institutions, how do campus community members and external stakeholders practice the principles of ethical and responsible use of AI?

This blog post is a high-level overview of this topic. I prompted ChatGPT with a series of prompts, and the majority of the content of this post is generated by ChatGPT.

The ethical and responsible use of Artificial Intelligence (AI) in higher education involves navigating a complex landscape of considerations to ensure that the deployment of these technologies is done in a manner that upholds the fundamental principles of fairness, transparency, and accountability, among others. The discussions around this topic can be enriched by understanding various perspectives and guidelines highlighted in academia and the broader educational sector.

Ethics Frameworks:

Ethics, a branch of philosophy dealing with what is morally right or wrong, has various frameworks that provide structured approaches to evaluating moral issues. These frameworks offer perspectives based on principles, values, or virtues, guiding individuals or organizations in ethical decision-making ​1​. Here are some frameworks and a brief description of each:

  • Virtue Ethics: Focuses on the inherent character of a person rather than on specific actions. It emphasizes virtues or moral character.
  •  Deontology: Based on rules and duties. It suggests an action is morally right if it follows a set rule or duty, regardless of the outcome.
  •  Rights and Responsibilities: Emphasizes the rights of individuals and the responsibilities that come with these rights.
  •  Consequentialism: Suggests that the morality of an action is judged solely by its consequences.
  •  Care-based Ethics: Stresses the importance of healthy relationships and the well-being of individuals and their interdependence ​2​.
  •  ETHICS Model: A theoretically grounded ethical decision-making model encompassing utilitarianism, moral relativism, and moral egoism as part of its framework ​3​.
  •  Framework for Ethical Decision Making (Markkula Center): A framework designed as an introduction to thinking ethically, aiding individuals in reflecting on the ethical aspects of their decisions ​4​.
  •  Framework for Making Ethical Decisions (Ethics at Work): Provides a summary of the major sources for ethical thinking and presents a framework for decision-making while considering multiple perspectives, ethical theories, and principles ​5​.

Ethical and Responsible Use of AI in Higher Education:

Understanding AI and its Implications:

It’s crucial for stakeholders in higher education, including educators, administrators, and leaders, to develop a foundational understanding of AI technologies. This understanding should extend to the design, implementation, and implications of AI in educational settings. A well-informed stakeholder community can engage in meaningful dialogue and decision-making processes concerning the ethical deployment of AI in higher education​1​.

Engaging stakeholders in higher education through workshops on AI technologies, offering courses on AI ethics, organizing public lectures by AI experts, developing online resources explaining AI, and establishing forums for open discussions help foster a foundational understanding of AI and its implications in educational settings.

Ethical Design, Implementation, and Governance:

Ethical considerations should permeate the design, implementation, and governance of AI systems. These considerations include leveraging existing legal and regulatory frameworks and developing new governance structures to ensure fairness, transparency, and accountability in AI applications within higher education ​1​.

Establishing ethics committees, developing ethical guidelines for AI research, conducting regular audits to ensure compliance with ethical and legal standards, incorporating mechanisms for accountability and redress, and engaging with external ethics boards form a framework for the ethical design, implementation, and governance of AI in higher education.

Addressing Equity and Discrimination:

AI presents unique challenges to equity in education, especially concerning commonly held views on discrimination. It’s vital to raise awareness about these challenges and work towards mitigating biases and promoting educational fairness ​1​.

Implementing bias detection and mitigation tools in AI systems, conducting regular training on equity and inclusivity, establishing channels for reporting AI-related discrimination, partnering with diverse communities, and collaborating with experts on equity are crucial steps toward addressing the challenges of equity and discrimination posed by AI.

Human Rights Perspective:

Ethically deploying AI in higher education also involves adopting a human rights perspective, ensuring that the technology does not infringe upon the rights of individuals but rather supports equitable access to educational resources and opportunities ​1​.

Ensuring adherence to privacy laws, conducting human rights impact assessments, establishing a human rights-centered framework for AI governance, collaborating with human rights organizations, and promoting transparency and informed consent are measures to align AI deployment with a human rights perspective in higher education.

Bias Mitigation and Privacy Protection:

The ethical aspect of AI use in higher education significantly revolves around addressing AI error, bias, discrimination, and data privacy. It’s essential to work towards reducing biases and protecting individuals’ privacy ​1​.

Implementing strict data anonymization and encryption standards, deploying explainable and auditable AI systems, establishing a robust data governance framework, conducting regular privacy audits, and engaging in ongoing dialogue with stakeholders is pivotal in mitigating biases and protecting privacy.

Human Responsibility:

Ethical AI underscores human responsibility in creating and deploying AI systems. The human element ensures that AI systems operate within ethical boundaries​ 2​.

Establishing clear lines of accountability, providing training for human overseers of AI systems, ensuring human-in-the-loop mechanisms in critical decision-making processes, encouraging ethical leadership, and promoting a culture of responsibility are steps to underscore human responsibility in the deployment and management of AI systems.

Promoting Digital Literacy:

Ensuring that educators and students have the necessary digital literacy to understand, interact with, and critically evaluate AI technologies is an integral part of ethical AI use in higher education ​1​.

Offering digital literacy courses, developing resources on digital rights and online safety, organizing digital literacy campaigns, establishing partnerships with tech companies for training, and creating platforms for sharing digital literacy resources are measures to foster digital literacy among educators and students.

Interdisciplinary Collaboration:

Addressing the ethical implications of AI in higher education necessitates interdisciplinary collaboration, drawing insights from ethics, law, social sciences, and other fields​ 3​.

Forming interdisciplinary committees, organizing cross-departmental workshops, encouraging joint research projects on AI ethics, establishing partnerships with other institutions, and promoting dialogues between academia, industry, and government are initiatives to foster interdisciplinary collaboration on ethical AI in higher education.

Promoting Ethical Practices in Classrooms:

Encouraging, practicing, and supporting the ethical and responsible use of AI in classrooms is pivotal, requiring multiple ways to think through the use of generative AI in teaching and learning​ 4​.

Integrating AI ethics discussions within course curricula, encouraging faculty to adopt ethical AI tools, organizing classroom discussions on real-world AI ethical dilemmas, encouraging students to undertake projects exploring the ethical dimensions of AI, and providing resources for teachers to explore ethical AI applications help in promoting ethical practices in classrooms.


Exploring the ethical and responsible AI use in higher education requires examination of the topic from various disciplines. The concept of FATE—Fairness, Accountability, Transparency, and Ethics—guides this exploration. Ethics frameworks act as roadmaps in understanding this complex topic. Stakeholders representing diverse campus communities should be involved in designing and implementing AI policies and practices. 


Leveraging ChatGPT’s Image Analysis for Enhanced Student Success: A Deep Dive into the 24×7 On-Demand Omni-channel Student Success Ecosystem

I finally got access to ChatGPT’s image analysis functionality today! I’ve seen social media posts about how users are finding ways to use this new feature. I wanted to know how ChatGPT can help me enhance the idea of a Student Success Support Model I introduced in this blog post. Here is the diagram of this concept.

The following is a series of prompts and ChatGPT responses to the concept/image above.

Prompt: Interpret the image.

The image depicts a diagram titled “24×7 On-Demand Omni-channel Student Success Ecosystem v2” created by Joe Sabado on 01/04/21. The diagram seems to be illustrating the various components and channels involved in a student success ecosystem. Here’s a breakdown:

  1. Communication Channels:
    • Platforms like Chatbot, Websites, Social Media have 24×7 availability.
    • Video Conferencing tools like Zoom and Microsoft Meeting.
    • Email, Text Messaging, Chat, Voice (phone), and Face-to-Face (on-premise) methods.
  2. Students are at the top, indicating they are the primary users or beneficiaries of the system. They interact with the various communication channels.
  3. Staff: Below the students are different staff roles that assist and interact with students:
    • Student Success Coaches
    • Case Managers who handle financial, mental/health issues.
    • General Staff
    • Faculty
  4. Tools & Technologies:
    • Personalized Self-Service: Appears to interact with a Digital Assistant (like Siri or Alexa).
    • Dashboard: Central interface for accessing various tools and data.
    • Customer Relationship Management (CRM): Software for managing interactions with students.
    • Data Analytics/Visualization: Offering descriptive, diagnostic, predictive, and prescriptive insights.
    • Data Lake (cloud): Centralized storage repository.
  5. Information Systems Integration via API: Various systems integrated for seamless data exchange:
    • Institutional Data (campus statistics)
    • Administrative Systems (e.g., Housing, Dining)
    • Learning Management System
    • Student Information System (SIS)
    • Academic Data (like Curriculum, grading)
    • Student Advising
    • Library
    • Student Engagement
    • Other Systems

Overall, the diagram showcases a comprehensive system that aims to provide round-the-clock support for students, integrating various tools, communication channels, and information systems. The design suggests a focus on student success, convenience, and holistic support.

Prompt: Suggest areas of gaps and opportunities for improvements.

  1. Single Point of Access: While there are multiple communication channels, it might be beneficial to create a unified portal or app where students can easily access all available resources, rather than navigating multiple platforms.
  2. Feedback Mechanism: There doesn’t seem to be a direct way for students to provide feedback on the services they receive. Integrating a feedback system can help in refining and enhancing the service quality.
  3. AI & Machine Learning: While there are chatbots and digital assistants, leveraging advanced AI and machine learning can further personalize student experiences, predict their needs, and proactively address potential issues.
  4. Integration of External Resources: There might be external resources or platforms (e.g., third-party study tools, online communities) beneficial for students. Consider integrating or partnering with them.
  5. Security & Privacy: With the integration of various systems and the use of a data lake, ensuring robust security measures and strict data privacy compliance is crucial.
  6. Professional Development for Staff: Regular training sessions for staff to familiarize them with new tools, methodologies, and best practices can enhance their efficiency and effectiveness.
  7. Offline Support: While there’s an emphasis on digital tools, ensuring that students who prefer offline or face-to-face interactions aren’t left behind is essential.
  8. Multilingual Support: Diverse student populations might benefit from multi-language support in chatbots, digital assistants, and other communication channels.
  9. Analytics & Reporting: The current analytics focus on descriptive, diagnostic, predictive, and prescriptive insights. Consider adding real-time analytics and creating dashboards that can highlight anomalies or areas of concern immediately.
  10. Accessibility: Ensure that all tools, especially digital platforms, are accessible to students with disabilities. This can include voice commands, screen readers, or other assistive technologies.
  11. Holistic Well-being: While there’s a mention of financial and mental/health support, consider adding more comprehensive well-being resources, including physical health, career counseling, and social well-being.
  12. Onboarding & Training: For new students or those not tech-savvy, an onboarding process or training modules on how to use the ecosystem can be beneficial.
  13. Continuous Improvement: Establishing a dedicated team or system for continuous assessment and improvement of the ecosystem will ensure it remains up-to-date and meets students’ evolving needs.

Remember, while these are suggestions based on the diagram, the real effectiveness and feasibility of these improvements would depend on the specific context, budget, and infrastructure of the institution or organization implementing this ecosystem.

Prompt: Suggest opportunities for effective application of Generative AI in this concept.

  1. Personalized Learning Paths: Generative AI can analyze students’ past performances, strengths, and areas of improvement to generate custom-tailored study plans or curriculum pathways. This can help each student focus on what’s most beneficial for their academic growth.
  2. Dynamic Content Creation: For topics where students frequently request more information or clarification, Generative AI can produce additional study materials, summaries, or explainer videos, thus enhancing the learning resources available.
  3. Simulated Scenarios: For courses that involve problem-solving, Generative AI can create an array of unique problems or case studies, allowing students to practice more diversely.
  4. Virtual Assistants: Beyond basic chatbot functions, Generative AI can be utilized to develop more sophisticated virtual assistants that can generate responses in natural language, assisting students with complex queries or creating interactive learning experiences.
  5. Automated Essay Scoring and Feedback: Generative AI can evaluate student essays or assignments, providing not just scores but also constructive feedback on areas of improvement, structure, and coherence.
  6. Enhanced Digital Interactions: Generative AI can produce realistic avatars or digital humans for more engaging virtual office hours or counseling sessions, especially when staff is unavailable.
  7. Predictive Analytics: Generative models can be used to simulate potential future scenarios based on current trends, helping institutions anticipate challenges or areas where intervention might be needed.
  8. Customized Communication: By analyzing student interactions and preferences, Generative AI can craft personalized messages, reminders, or notifications, making communication more relevant and engaging.
  9. Collaborative Projects: Generative AI can be used to create virtual environments or scenarios where students can collaborate, mimicking real-world challenges and tasks related to their field of study.
  10. Creative Arts & Design: For courses in the arts, design, or media, Generative AI can be employed to suggest designs, compose music, or even co-create artworks, providing students with innovative tools to explore their creativity.
  11. Language Translation: Generative AI can assist in real-time translation, ensuring that non-native speakers can access content in their preferred language, thus improving inclusivity.
  12. Cultural Adaptation: For institutions with a diverse student body, Generative AI can be trained to understand cultural nuances and adapt content or communication to be more culturally sensitive and relevant.
  13. Continual Learning: The system can leverage Generative AI to keep evolving its knowledge base, ensuring that it remains current with the latest research, trends, and student needs.

Integrating Generative AI into the student success ecosystem can significantly enhance the quality of education and support offered. However, it’s crucial to approach its application ethically, ensuring data privacy, minimizing biases, and maintaining transparency with students about how their data is used.

Prompt: What student-success, technology adoption, pedagogical, and other frameworks should be considered in implementing the concept in the diagram?

  1. Student-Success Frameworks:
    • Tinto’s Model of Student Retention: Focuses on the interplay of social integration, academic integration, and student persistence.
    • Astin’s Theory of Involvement: Emphasizes the significance of student involvement in academic and extracurricular activities for success.
  2. Technology Adoption Frameworks:
    • Technology Acceptance Model (TAM): Assesses how users come to accept and use a technology based on perceived usefulness and perceived ease of use.
    • Unified Theory of Acceptance and Use of Technology (UTAUT): Provides a unified view of user acceptance of technology, combining elements from multiple models.
  3. Pedagogical Frameworks:
    • Constructivist Theory: Prioritizes learner-centered approaches where students actively construct their own knowledge.
    • Bloom’s Taxonomy: Classifies learning objectives into hierarchical levels from remembering facts to creating new concepts.
    • Universal Design for Learning (UDL): Focuses on creating curricula that meets the needs of all learners from the start.
  4. Data and Analytics Frameworks:
    • Learning Analytics: Utilizes detailed data collection and analysis to understand and optimize learning and the environments in which it occurs.
    • Predictive Analytics for Student Success (PASS): Uses historical data to predict future outcomes, such as student dropout risk.
  5. Implementation and Change Management Frameworks:
    • Kotter’s 8-Step Change Model: Provides a structured approach to leading organizational change.
    • ADKAR (Awareness, Desire, Knowledge, Ability, Reinforcement): Focuses on the people side of change to ensure successful adoption.
  6. Stakeholder and Communication Frameworks:
    • RACI (Responsible, Accountable, Consulted, Informed): Clarifies roles and responsibilities during a project or process.
    • Stakeholder Analysis Matrix: Assesses the interest, influence, and impact of stakeholders.
  7. Integration and Interoperability Frameworks:
    • Enterprise Service Bus (ESB): Aims to integrate different systems and applications in an organization.
    • API-first Design: Designs the API specifications first before any other code, ensuring seamless integration between systems.
  8. Security and Privacy Frameworks:
    • ISO/IEC 27001: Offers requirements for an information security management system.
    • FERPA: U.S. federal law that protects the privacy of student education records.

URLs for references:

Using ChatGPT’s image analysis feature as I did in this post is another example of how Gen AI can be used to ideate existing ideas.

What other use cases in higher education can you think of?

Template – Campus Guideline for the use of Artificial Intelligence

Since the introduction of ChatGPT in November 2022, higher education institutions have developed policies and guidelines. However, this list of resources and the following documents/web pages focus primarily on teaching and learning, specifically the ethical use of Generative AI. Perhaps not surprising is that it is infrequent to come across robust campus policies and guidelines to inform the entire campus community (staff, students, faculty, and researchers) on the appropriate use of this technology. One university that may have the most coordinated campus AI response is the University of Michigan. If other universities have as comprehensive a strategy as U of Michigan, please share it at so I can add them to the collection above.

As one always looking for opportunities to contribute to the thoughtful adoption of Artificial Intelligence in higher education, I developed a template that higher education institutions could consider using to promote their campus AI policies and guidelines.

Please let me know other sections and contents to include in this document:

Template – Campus Guideline for the use of Artificial Intelligence

Responsible Generative AI Use: Balancing Innovation and Responsibility at 


Educause Annual Conference – Generative AI Sessions Themes

What are the themes of the 20 Generative AI/ChatGPT sessions at the Educause Conference 2023? As a higher education IT professional and one who has been exploring the uses and implications of GenAI in higher education, I was curious about what to expect. I will attend the national conference, so I am excited about what to expect. I prompted ChatGPT to analyze the themes for the following sessions; the results are below.  Here are the sessions with descriptions.

Core Themes:

  1. AI Ethics and Equity: A recurring theme, focusing on the moral and fairness aspects of AI.
  2. AI in Education: Examines the transformative potential of AI in teaching and learning.
  3. AI Strategy and Leadership: Discusses the integration of AI into institutional strategies.
  4. AI in Technical and Operational Efficiency: Looks at how AI can streamline various institutional processes.
  5. AI’s Current and Future Impact: Evaluates both the immediate and long-term effects of AI in education.

Unique Themes:

  1. AI and Extended Reality: Explores the intersection of AI with virtual and augmented reality.
  2. AI in Assessment: Discusses the role of AI in academic assessments and integrity.
  3. AI as a Collaborative Tool: Views AI not just as a tool but as a partner in the educational process.

I wanted to go further and prompted ChatGPT to analyze themes for each session. Here are the results.

Monday, October 9, 2023

  • AWS Powerful Partnerships Built on Data Get Results
    • Theme: Data-Driven Decision Making
      • Description: Utilizing data analytics and AI to solve challenges in higher education.
    • Theme: Cross-Institutional Collaboration
      • Description: Building effective teams across institutions to foster innovation.
  • Generative AI as a Deep Agent of Chaos for Higher Education
    • Theme: AI in Education
      • Description: The impact of AI on various aspects of higher education.
    • Theme: Futurism
      • Description: Forecasting the future impact of AI on higher education.

Tuesday, October 10, 2023

  • Generative AI Implications for the Future
    • Theme: AI Ethics and Equity
      • Description: Discussing ethical and equity issues related to AI.
    • Theme: AI Research and Development
      • Description: Proposed research agenda for understanding AI’s impact.
  • Generative AI: 5 Questions for Higher Ed CIOs
    • Theme: AI Adoption Strategies
      • Description: Different approaches to adopting AI in educational institutions.
    • Theme: IT Efficiency
      • Description: Leveraging AI for operational efficiency.
  • Generative AI: the Good, the Bad and the Bias
    • Theme: AI Ethics
      • Description: Ethical considerations in the deployment of AI technologies.
    • Theme: AI Limitations
      • Description: Understanding the limitations of AI in practical applications.
  • EDUCAUSE Leadership Series
    • Theme: AI in Leadership
      • Description: How leaders can navigate the challenges and opportunities of AI.
    • Theme: AI in Pedagogy
      • Description: Educational opportunities related to AI.
  • Boom or Bust? The Future of Generative AI in Higher Education
    • Theme: AI Impact Assessment
      • Description: Debating the potential long-term impacts of AI on higher education.
  • Generative AI: The Future of Learning in Higher Education is Now
    • Theme: AI in Learning and Development
      • Description: How AI can personalize learning and improve efficiency.
  • Beyond ChatGPT: Exploration of AI in Higher Education
    • Theme: AI Preparedness
      • Description: Preparing educational institutions for the impact of AI.
    • Theme: AI Pros and Cons
      • Description: Weighing the benefits and drawbacks of AI in education.
  • Intersection of Artificial Intelligence and Extended Reality
    • Theme: AI and Extended Reality
      • Description: The synergistic effects of AI and virtual reality in education.

Wednesday, October 11, 2023

  • Assessment in the Age of Generative AI
    • Theme: AI in Assessment
      • Description: The impact of AI on educational assessments.
    • Theme: Academic Integrity
      • Description: Concerns about authenticity and integrity in AI-driven assessments.
  • From Innovation to Transformation
    • Theme: AI-Driven Pedagogy
      • Description: Innovative teaching strategies using AI.
  • Risky Business: Can Workflow and Artificial Intelligence Bridge the Enrollment Cliff?
    • Theme: AI in Enrollment
      • Description: Using AI to improve student enrollment processes.
    • Theme: Budget Management
      • Description: Financial considerations in AI adoption.
  • AI Together: Developing Usage Policy for Generative AI with Students
    • Theme: AI Policy Development
      • Description: Creating guidelines for AI usage in educational settings.
  • Generative AI Is More than a Tool- It’s a Digital Collaborator
    • Theme: AI as a Collaborative Tool
      • Description: Viewing AI as a partner in the educational process.
  • The End of Business as Usual: Embracing Generative AI within Higher Ed
    • Theme: AI Strategy and Leadership
      • Description: Integrating AI into institutional strategies and leadership.
  • Bias, Stereotypes, and Hallucinations in Generative AI
    • Theme: AI Bias and Stereotypes
      • Description: Addressing issues of bias in AI outputs.
  • Generative AI in Higher Education: Implications for the Present and Future
    • Theme: AI’s Current and Future Impact
      • Description: Assessing the immediate and long-term effects of AI in education.
  • The Future of Generative AI: Take Action Today
    • Theme: AI Future Planning
      • Description: Preparing for the future impact of AI in education

Thursday, October 12, 2023

  • Generative Pre-Trained Transformer: A 2023 Odyssey in Technical Support
    • Theme: AI in Technical Support
      • Description: Using AI to enhance IT support services.

Analyzing themes of conference sessions is yet another use case for ChatGPT. I could have also gone further to do some research on the presenters, their work and stance on Generative AI, and generate some questions to ask at their sessions like I did in this blog post.

I hope to see you at the conference to share ideas on Generative AI and Higher Education or let’s connect via email at

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