Joe Sabado – Higher Education & Technology Leadership

As we navigate the evolving landscape and adoption of artificial intelligence (AI) in higher education, it’s become increasingly clear that a guiding framework is needed to help chart these efforts.

Since the launch of ChatGPT in November 2022, I’ve been closely observing how higher education institutions adapt to and embrace AI technologies. My observations suggest the vast potential of AI to transform our campuses, but it has also highlighted the complexities of effectively and ethically integrating these technologies.

In co-leading the AI Community of Practice at our institution, I’m excited about various AI-related initiatives emerging across departments. These are driven by genuine enthusiasm and a desire to innovate, and the campus can further enhance through a coordinated institutional-level effort. In addition, it’s as essential to acknowledge and learn from other institutions’ AI efforts to accelerate our campus initiatives. These observations led me to create the Higher Education AI Capability Maturity Model specifically for higher education—an initial attempt to develop a comprehensive framework that can unify disparate AI efforts cohesively and strategically.

This model is a starting point, a compass to guide campus leaders and communities through the initial stages of AI integration in higher education. It aims to evaluate current AI initiatives, foster discussions about future goals, and provide a benchmark for measuring progress against other institutions. Importantly, this model is designed to evolve through collaboration and shared insights from the broader higher education community.

I recognize that this initial model is beginning a journey toward a more robust and comprehensive framework. I invite the higher education community to join me in this conversation. I’m actively seeking ideas, feedback, and input to refine and expand this model, making it truly reflective of the diverse needs and aspirations of higher education institutions everywhere.

This effort is not just about creating a tool for assessment or benchmarking; it’s about fostering a culture of collaboration and innovation in using AI on our campuses. Let’s collaborate on improving this model and how we can ensure that our approach to AI is strategic, inclusive, and aligned with the core values of higher education.

Draft Higher Education AI Capability Maturity Model

Please get in touch with me at joepsabado@gmail.com if you’re interested in working together.

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:

• Since the public introduction of ChatGPT in November 2022, I have been monitoring how higher education institutions respond to Gen AI.
• I have analyzed various policies and guidelines, identifying common themes and strategies.
• The significant presence of Gen AI at the Educause Annual Conference, both in sessions and the exhibit hall, further underscores its growing relevance.
• My own experimentation with Gen AI has demonstrated its practical benefits in daily work, learning, and even in the development of an IT Professionals Mentorship Program.
• Additional insights I have been gleaned from online courses, industry announcements, and campus events.

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.

Roadmap

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.

References:

• Higher Education Guidelines and Policies
• Gen AI-Related Trainings Completed
• Comprehensive Classroom Policy on the Use of Artificial Intelligence (AI)
• Review of Gen AI Strategies/Implementation of AAU Institutions

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.

Conclusion:

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. 

References:

• NCSEHE: Artificial intelligence, ethics, equity, and higher education
• Fierce Education: Artificial Intelligence in Higher Education: Benefits and Ethics
• Inside Higher Ed: Toward Ethical and Equitable AI in Higher Education
• Cornell University: Ethical AI for Teaching and Learning
• Penn State University: What are Ethical Frameworks?
• Counseling: The ETHICS Model
• Markkula Center for Applied Ethics: A Framework for Ethical Decision Making
• Ethics at Work: A Framework for Making Ethical Decisions

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:

• Tinto’s Model of Student Retention
• Astin’s Theory of Involvement
• Technology Acceptance Model (TAM)
• Unified Theory of Acceptance and Use of Technology (UTAUT)
• Constructivist Learning Theory
• Bloom’s Taxonomy
• Universal Design for Learning (UDL)
• Learning Analytics
• Predictive Analytics for Student Success (PASS)
• Kotter’s 8-Step Change Model
• ADKAR Change Management Model
• RACI Matrix
• Stakeholder Analysis Matrix
• Enterprise Service Bus (ESB)
• API-first Design
• ISO/IEC 27001
• FERPA

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?

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 joepsabado@gmail.com.

The higher education IT service catalog was developed in response to the challenges posed by the diverse and changing user populations, mixed central and distributed services, and the collaborative nature of the sector.

https://www.educause.edu/working-groups/papers/2019/the-higher-education-it-service-catalog-second-edition/the-higher-education-it-service-catalog-model

Furthermore, the catalog serves as a framework to identify potential uses of ChatGPT or other Generative AI to enhance the quality of IT service delivery. The document provides potential uses of ChatGPT for each service area, enabling institutions to leverage AI technology for improved service provision.

https://docs.google.com/document/d/1vypjTMDClqloWDGkkZHiLkvNVvIp7KjLT7r39GdROu8/edit?usp=sharing

As IT leaders and practitioners in higher education, it is essential in our roles to have a fundamental understanding of those impacted by the services and systems we provide. This understanding is a cornerstone for enhancing user experiences, enabling successes, and fostering the achievement of institutional goals. In addition, adopting a framework that ensures equitable access to resources and incorporates fairness into every process is essential.

A faculty member inspired me with the idea of viewing the impact of the work we do in IT and thought, “Joe, it would be nice to understand the experiences of our staff and students so we have some insights into their challenges and we can find opportunities ourselves to assist them. I would like to know that my student employees are getting paid on time and that I can assist a student who may have issues outside the classroom by knowing who to contact in student affairs.“

An international student (female Indian) also shared a frustrating and heartbreaking experience. I spoke with her about her experience on campus, specifically related to IT. She said, “I am a Computer Science Ph.D. student about to graduate. I feel like an impostor sometimes because, throughout my time here, I couldn’t even provision a computer to do my research at this university. The systems are so locked down I have to ask for an IT staff to create a virtual system on my behalf.” She also mentioned that when she finally got access to systems, she didn’t have instructions on installing software to conduct her work and had to figure it out independently.

Working for a campus that is a minority-serving institution (MSI), designated as Hispanic Serving Institution and Asian American Native American Pacific Islander Serving Institution (AANAPISI), as IT providers, we must keep this commitment to diversity, equity, inclusion, and belonging at the forefront of the solutions we implement and in how we provide our services. Access to digital technology as a basic need came to light during the pandemic when our students were required to attend courses and conduct research away from campus. The digital divide became apparent early on when we had to provision Chromebooks and assist students in finding affordable internet access in their local residences. 

The experiences above led me to ask this guiding question:

How does information technology positively impact the success of the university by providing the utmost support for staff, faculty, students, and administrators along their life cycles and touchpoints with the university, and in alignment with their unique desires and needs (“Jobs To Be Done”),  to help them achieve their goals and contribute to the overall mission of the university?

Understanding our users involves mapping out the desires, needs, aspirations, and success parameters of various university constituents, namely undergraduate and graduate students, staff, faculty, researchers, and administrators. A general understanding of the unique lifecycles and university touch points for the various community members is essential for delivering IT services and solutions that positively contribute to the experience and success of each of these communities. It is imperative, however, to view this understanding through a lens that places equity and fairness at the forefront. This approach aspires to assure every user, regardless of their backgrounds, roles, or circumstances, experiences equal and fair access to technology resources and support. IT providers must align our procedures and policies with these principles, strengthening the commitment to equal opportunities within the university communities.

Incorporating user understanding has numerous benefits for IT providers and those we serve. This user-centric approach assists us in designing and delivering technology solutions that appropriately meet user requirements, ensuring technology supports users’ needs and purposes and enhancing their overall experience. User understanding contributes to increased adoption and engagement, enabling the creation of solutions that resonate with users’ needs and preferences. Finally, understanding users’ backgrounds, contexts, and goals empowers IT providers to communicate and offer support more effectively.

As IT providers, we must incorporate social justice principles into our approach. We should strive for equitable access, ensuring all users have equal access to technology resources and support, promoting inclusivity and social justice. We should aim for transparency in decision-making processes and uphold data privacy and security to maintain trust and respect the rights of individuals within the university community.

By weaving together user understanding, knowledge of their lifecycles, university touchpoints, and social justice principles, we can enhance our effectiveness in contributing to our user communities’ success. We can have a better chance to offer tailored, inclusive, and equitable technology solutions, fostering a culture of fairness and justice. In adopting these principles, we in IT can realize the potential values and provide maximum contributions to the university.

The following are high-level representations of each community’s lifecycles, touchpoints, desires and needs, and success metrics. Included as well is a campus perspective.

As an IT leader, our commitment to social justice including diversity, equity, and inclusion and understanding those we serve are through is through our consistent actions, learning from experience and education, and explicitly stating our commitments. For IT leaders, Educause provides DEI resources and a commitment to diversity statement. Here is my personal commitment. Furthermore, understanding our user communities is by volunteering in non-technical roles such as reading admissions applications, mentoring students and staff, teaching introduction courses, and serving on search committees. Here are some of the volunteer work I have committed to through the years. It also includes actively supporting our staff to do the same.

By understanding the unique needs of our diverse user communities and incorporating principles of social justice into our approach, IT providers in higher education can play a vital role in promoting an inclusive and fair university environment. This approach helps to create tailored, equitable technology solutions that empower users to achieve their goals, fostering a culture of fairness and justice within the university community.

The images above can be downloaded here:

Note: Concept by Joe Sabado and content of the lifecycles, desires and needs, and success metrics primarily provided by ChatGPT and other sources.

As an IT leader in a higher education institution overseeing critical, including COVID-19-related, systems, I appreciate the importance of IT being at the table and data-informed decision-making. A particular incident during the height of the COVID-19 pandemic highlighted the importance of IT representation in the institution’s core operations.
When a monthly process required a reboot of some systems, it became a source of concern when a well-meaning and concerned staff member, a campus COVID-19 coordination team member, reported that the system would be down for two days. This misinformation caused a mild panic that could have led to months of re-work of our systems, pausing other projects, hundreds of hours from our staff, and frustrations.
Fortunately, having someone in IT explain the situation corrected the misinformation. At the request of the coordination committee, I joined their team meeting to ensure our team’s commitment to addressing their concerns and to provide our perspective. To analyze the impact of the downtime, my team and I reviewed the logs and used an application that alerts downtimes. We discovered the systems were only disrupted for five minutes, much less than the reported two-day outage. This incident demonstrated the importance of data-informed decision-making in addressing complex issues.
It was clear that there was a need for collaboration between IT and our partners to address the issue. The incident highlighted the importance of having IT representation in the institution’s core operations. Electronic medical records, testing, and reporting are complex systems that require constant monitoring and attention. I requested to my supervisor that I become a standing member of the committee moving forward, which she approved. As part of the committee, I could address any concerns in real-time and better understand the actions requested by my team.

Our organization has emphasized the importance of collaboration and data-informed decision-making. Only through working collaboratively towards a solution could we identify the root cause of the issue and take steps to prevent it from recurring. We have also explored ways to make our systems more redundant and have coordinated with the campus for communication before any potential downtime.

The incident we experienced during the COVID-19 pandemic highlighted the criticality of having IT at the table and data-informed decision-making. Mis-information can cause panic, frustration, and waste resources. By working collaboratively, we can identify and address issues promptly, ensuring that operations run smoothly and effectively.

How can we effectively provide service to those we serve without spending time with them and understanding their world?

I’ve been a staff advisor for student organizations for the last two decades and learned a very important lesson a few years ago. There was a year when I assumed I could maintain my relationship with the organizations I advise and the incoming student leaders and their needs and wants because I’d been their advisor for many years before that. I was caught off guard when I sensed a shift in attitude; explaining was challenging, but I felt it. I had to ask my colleagues if they were experiencing a change in student attitudes, and they confirmed my observation. It was neither bad nor good; it was just different. Another time, students started talking about “clickers,” and I had no clue what they were talking about.

One time, I was playing Pokémon with some students, and my supervisor, the Vice Chancellor for Student Affairs, happened to see us. The students thought I would be in trouble, but it’s quite the opposite. She was glad I spent time with students and pleased I played Pokémon walking around on campus so “I can experience paths where students go that I miss otherwise.” One of the most enduring pieces of advice she gave me is that as an administrator,” one of the most important insights we have to represent our students are the one-on-one conversations we have with them.” It’s also important to note that administrators probably spend time with student leaders and those already engaged. But the students we need to reach out to feel marginalized and suffer in silence because they either don’t know the services offered or don’t trust the campus.

Some communities require trust for staff/faculty/administrators to be genuinely invited into their communities. Often, it may take me more than a couple of years, even getting the urging of their peers comfortable with me, for younger students to meet with me. When I meet with them, they regret not accepting my offer for lunch sooner after finding out that I’m not as intimidating as they had presumed. I asked them why they didn’t meet with me sooner. They tell me they are trying to figure out how to respond to staff/faculty asking them to meet for casual lunch or coffee because they haven’t experienced that offer.

There’s a saying in the student affairs world to “meet students where they are,” which means making ourselves available at their events or just perusing what’s going on in social media to get a pulse of what’s going on. I follow the Facebook parents page, TikTok, and Twitter and am also alerted to Reddit posts on an unofficial campus subreddit. Even today, I’ve heard some colleagues reticent to using social media, but as my supervisor said, “You’re missing half the conversation if you don’t engage online.”

When we make decisions based not on students’ input or lack of understanding about their current world, not what we experienced as undergrads many decades ago, even with the best intentions, they can harm our students.

Let me tell you the story of a person who has gone through many painful experiences throughout their life but has used these experiences to serve others. This person was mocked and made fun of as a young kid when they immigrated to the United States at the age of 11. This traumatic taunting led them to stuttering and a paralyzing fear of public speaking for half their life.

However, decades later, this person was able to overcome their fear of public speaking and stuttering. They decided to share their painful experiences and vulnerabilities with others, specifically with first-generation and marginalized students. They have had the privilege of being a keynote speaker for First-Generation Graduation, speaking at high school conferences, and Pilipino Graduation ceremonies. They have even conducted public speaking workshops for students who share their background as an immigrant.

This person’s story doesn’t end there. During their undergraduate years, they experienced painful moments as a student leader. As an inexperienced student leader, they made many mistakes and felt shunned by the community they cared about. These painful memories stayed with them for decades. But instead of letting these experiences defeat them, this person used them to provide guidance to the students they advised in the last two decades.

Recently, a group of student leaders this person advised found themselves in a difficult situation that led them to question themselves and their ability to lead. The situation even impacted their mental health, relationships, and academics. This person could relate to their struggles and offered them the perspective of someone who had faced similar challenges. They shared with the students that the moments of struggle and failure contributed to their growth and the lessons they still use today in their leadership position at their university.

This person’s story is a testament to the fact that our painful experiences and weaknesses can be used to serve others. By sharing our vulnerabilities, we can offer guidance and support to those who are going through similar struggles. This person’s journey shows that our struggles can contribute to our growth and help us become better versions of ourselves.

That person is me.

I would like to know how predictions aided by Bing AI would perform the winners of the 2023 NCAA Men’s Basketball Tournament. I have not typically participated in completing my predictions, so this is an unscientific exercise from a novice who is curious about how a Bing AI-aided picks would fare. Here are the picks. The Final Four teams include Alabama, Tennessee, Penn State, and Gonzaga. Alabama an Gonzaga play for the championship with Alabama as the national champion.