ChatGPT, AI, and Higher Education

The following is a list of personal blog posts focusing on the applications and implications of ChatGPT in higher education. The topics covered range from the potential benefits and applications of ChatGPT in student learning and teaching to the risks and challenges of implementing it in campus cybersecurity and student conduct. Other topics include the impact of ChatGPT on higher education, its coding functionality, and its use in social justice and Filipino-American history education. Some posts also explore the future of higher education and the role of ChatGPT in it, and its potential to serve stakeholders through the Jobs-To-Be-Done theory.

Collection of higher education and ChatGPT resources, events, and articles.




ChatGPT’s General Use and Implications in Higher Education

ChatGPT as Training/Quiz Generator

ChatGPT as a Learning Tool

ChatGPT and Cybersecurity

ChatGPT for Workplace Efficiency



“Social Imagination” as AI Framework

How can we discuss AI decisions that center on the human experience and societal impacts and consider the future implications of our actions? 

This blog post introduces the concept of Sociological Imagination as a framework for technologists, policymakers, affected communities, and leaders to take a holistic and balanced approach to Generative AI discussions and decisions. 

The concept of sociological imagination, which I interpret as the intersection of one’s identity with society and history, was coined by C. Wright Mills in 1959. He defined it as the ability to see the relationship between individual experiences and more significant social influences. Dr. John Cruz introduced me to the concept in a UCSB Filipino-American History course as an undergraduate student in 1992. It has shaped how I have examined my the intersectionality of my identities and leadership approach. The concept provides context to and the impact of my decisions not just today but for the future.

Maxine Hong Kingston, a renowned Asian American author, reminded the audience of our role and the potential of our actions to impact future generations at a UCSB Asian American Studies event. When asked about her view on activism in the 1960s compared to the present, her response highlighted that our actions might have impacts that extend beyond our generations. This idea reinforces the belief that “the past constrains the present and the future is shaped by the present,” underlining our opportunity—and responsibility—to shape the future, particularly in AI, with the help of Sociological Imagination.

At a recent University of California Artificial Intelligence Congress, one message was clear: AI’s future is not inevitable, and we have the opportunity and responsibility to shape that future intentionally. 

On social media, at universities, and across the higher education sector, discussions about AI cover a wide range from personal to societal impacts. These discussions range from detailed, tactical aspects to broad, strategic considerations, including AI’s potential to improve efficiency or even transform higher education completely. Often, these varied discussions point out that some perspectives might be too narrow, overlooking larger societal effects and future outcomes. At the same time, others may be too broad, missing crucial personal implications.

Another ethical debate or emphasis exists around AI and its role in the workplace – whether to replace or enhance humans. This discussion focuses on the potential for AI to either automate jobs, leading to job displacement or augment human workers’ capabilities, thereby increasing productivity and job satisfaction. This debate is critical as it directly affects individual livelihoods and organizational effectiveness, and it raises questions about the kind of future we want to create through the implementation of AI technologies.

This observation highlights the need for a holistic and balanced approach. This approach should look at both the small details and big picture of using AI and what our choices will mean for the future. It’s important to handle immediate problems and chances while also thinking ahead about the long-term impacts these technologies will have on people, places, and society.

I propose using Sociological Imagination as a framework to guide our conceptualization and implementation of Generative AI.

Sociological imagination can help us understand AI better by showing its wide effects and possibilities. For example, in healthcare, it can show us how AI might change doctor-patient relationships and who gets healthcare. In education, it helps us see how AI could affect how students learn and teachers teach. This approach gives us a broader view of AI’s role in society. Here are some ways to frame AI from a sociological imagination perspective.

1. Bridging Individual and Collective Experiences

Sociological imagination helps in connecting individual experiences with AI to collective societal outcomes. For example, personal encounters with AI-driven services can inform broader discussions about privacy, data security, and user consent. When we look at how people interact with AI, it’s important to think about how these interactions shape what we expect from technology. We need to develop AI systems that protect individual freedom and benefit everyone in society.

2. Highlighting Socioeconomic Impacts

This framework encourages examining the socioeconomic disparities that AI might exacerbate or mitigate. Talks should focus on how AI can make things faster and give more people information. But, we must also consider the downsides, like losing jobs and growing gaps between rich and poor. Using sociological imagination, stakeholders can strategize on deploying AI to address rather than deepen social and economic divides.

3. Incorporating Historical Lessons

Sociological imagination involves learning from past technological and social shifts to predict and shape AI’s impact. Studying history helps us see how technology has transformed industries and societies. It teaches us how to manage changes, reduce harm, and take advantage of new opportunities. This historical perspective can be crucial in anticipating and strategically preparing for AI’s long-term consequences.

4. Envisioning Future Scenarios

Utilizing sociological imagination in AI discussions also means thinking about the future societal implications of AI integration. It aids in envisioning future scenarios based on current trends, from romantic to dystopian. This forward-looking approach helps in designing AI policies and technologies that are adaptable and resilient, ready to handle unexpected societal changes and challenges.

5. Ethical and Cultural Considerations

AI discussions guided by sociological imagination naturally include ethical and cultural considerations, recognizing the diverse contexts in which AI operates. These discussions involve questioning who is programming the AI and whose values are reflected in its operations. The goal is to ensure that AI systems are culturally sensitive and ethically designed, promoting fairness and avoiding biases that can harm underrepresented groups.

6. Facilitating Multi-stakeholder Dialogues

Sociological imagination fosters inclusive dialogues involving multiple stakeholders—technologists, policymakers, affected communities, and ethicists. It plays a crucial role in understanding the intersection of individual experiences and broader social processes, fostering a sense of inclusion and understanding in these discussions.

Understanding sociological imagination is key to seeing how AI fits into our lives. This idea connects our personal experiences with the larger society. It pushes us to think about both small details and big effects of AI. By learning from history and looking ahead, we get a full view of AI’s impact on jobs, fairness, and cultural differences.

Using sociological imagination, we can ensure that AI is not only advanced but also fair and inclusive. It encourages open talks with tech experts, policymakers, and everyone else to make AI that meets personal and society needs. This way of thinking helps us make decisions about AI that improve our lives and tackle big social issues. So, let’s use sociological imagination as a guide, share our thoughts, and work together towards a better AI future.

Measuring the Success of Generative AI Adoption in Higher Education

In the rapidly evolving landscape of higher education, Generative AI (Gen AI) is emerging as a transformative technology with the potential to revolutionize teaching, learning, and institutional operations. However, as with any significant technological adoption, implementing Gen AI in colleges and universities comes with challenges, resource requirements, and potential unintended consequences.

As higher education institutions consider embracing Gen AI, it is crucial to carefully assess its value and impact. By understanding the costs, benefits, and risks associated with Gen AI, decision-makers can make informed choices that align with their institution’s mission, goals, and resources.

As I develop a proposed Gen AI capability maturity model and help lead our university’s AI efforts, a critical question on my mind is, “How do we measure the success of our Gen AI adoption and utilization in higher education?” This question is essential for understanding the value and impact of Gen AI investments and ensuring that these technologies effectively support institutional goals and student success.

Key Questions to Consider:

When evaluating the value of Gen AI in higher education, there are several key questions to consider:

  1. What specific problems or opportunities can Gen AI address in our institution?
  2.  How will adopting Gen AI contribute to our educational mission and student success?
  3.  What resources (financial, technological, human) are required to implement and maintain Gen AI systems?
  4.  What are the potential opportunity costs of investing in Gen AI compared to other institutional priorities?
  5.  How can we measure the effectiveness and impact of Gen AI on student learning outcomes and institutional performance?
  6.  What are the possible unintended negative consequences of Gen AI adoption, such as ethical concerns, privacy issues, or widening digital divides?
  7.  How can we mitigate the risks and challenges of Gen AI while maximizing its benefits?
  8.  How do we measure success in implementing Gen AI in higher education?

Measuring Success: Outputs and Outcomes

Measuring success is critical to assessing Gen AI’s value in higher education. One effective approach is to adopt the concept of outputs and outcomes. When seeking a potential framework for assessing success, one concept that comes to mind is ProSci’s definition of success, which includes project objectives and organizational benefits.

Project objectives focus on the project’s aims, how it will contribute to solving a problem or realizing an opportunity, what it will produce or enable, and how we will know when the objectives have been achieved. These objectives are closely tied to the outputs, which are the direct products or deliverables resulting from the implementation of Gen AI, such as AI-powered chatbots, personalized learning systems, or AI-assisted research tools.

On the other hand, organizational benefits represent what the institution gains from the project. They address the problem or opportunity that the project aims to solve or realize for the organization and define the benefits that will be achieved if the problem is solved or the opportunity is realized. These benefits are often linked to the outcomes: the broader impact and positive changes that the outputs bring to the institution, students, and stakeholders. Outcomes can be measured by establishing key performance indicators (KPIs) and regularly monitoring progress.

Examples of Outputs and Outcomes:

Here are some specific examples of how project objectives and organizational benefits relate to outputs and outcomes in the context of Gen AI and higher education:

Project Objectives and Outputs:

  • Objective: Develop an AI-powered chatbot to assist students with course-related queries and administrative tasks.
  •  Output: A fully functional chatbot integrated into the university’s website and learning management system.
  •  Objective: Implement an AI-driven personalized learning system that adapts to individual student needs.
  •  Output: An intelligent tutoring system that provides customized learning paths and resources based on student performance and preferences.
  •  Objective: Create an AI-assisted research tool to help faculty and students identify relevant literature and generate insights from large datasets.
  •  Output: An AI-powered research platform that integrates with academic databases and provides advanced search, analysis, and visualization capabilities.

Organizational Benefits and Outcomes:

  • Benefit: Improved student support and engagement
  •  Outcome: The reduced workload for faculty and administrative staff in addressing routine student queries led to increased student satisfaction and retention.
  •  Benefit: Personalized learning experiences
  •  Outcome: Improved student learning outcomes, higher course completion rates, and increased student motivation and engagement in their studies.
  •  Benefit: Enhanced research productivity and impact
  •  Outcome: Research processes were more efficient and effective, leading to higher-quality publications, grant success rates, and institutional reputation.

Measuring Achievement:

  • Chatbot objectives can be measured by the number of student queries successfully handled, reduced response time, and student satisfaction scores.
  •  Personalized learning system objectives can be evaluated through improvements in student grades, course completion rates, and student feedback on the adaptive learning experience’s effectiveness.
  •  AI-assisted research tool objectives can be assessed by the number of users, the volume and relevance of literature identified, the insights generated from data analysis, and user satisfaction with the platform.

Realizing Benefits:

  • Improved student support can be observed through surveys measuring student satisfaction, reduced dropout rates, and increased student engagement metrics.
  •  Personalized learning benefits can be realized through data analysis showing improved student performance, increased course completion rates, and higher student retention and graduation rates.
  •  Enhanced research productivity can be demonstrated by tracking the number and quality of publications, grant success rates, and the institution’s research ranking and reputation.

Steps to Measure Success:

To effectively measure the success of Gen AI initiatives, institutions should follow these steps:

  1. Define clear objectives and desired outcomes for each Gen AI project or initiative.
  2.  Identify specific outputs that contribute to achieving those outcomes.
  3.  Establish metrics and key performance indicators (KPIs) to track progress and measure the effectiveness of outputs and outcomes.
  4.  Regularly monitor and evaluate the performance of Gen AI systems against these metrics and KPIs.
  5.  Use data-driven insights to make informed decisions about Gen AI initiatives’ continuation, modification, or termination.

Tracking Progress with OKRs:

One practical framework for tracking progress in pursuing the intended outputs and outcomes is Objectives and Key Results (OKRs). OKRs are goal-setting and management tools that help organizations define and track objectives and outcomes. The framework consists of two main components:

  • Objectives: High-level, qualitative goals that are ambitious and align with the organization’s mission and strategy.
  •  Key Results: Specific, quantifiable measures demonstrating progress towards achieving the objectives. Key results should be measurable, time-bound, and realistically achievable.

By adopting the OKR framework, higher education institutions can effectively monitor the progress of their Gen AI initiatives and ensure that they are on track to deliver the desired outputs and outcomes. The benefits of using OKRs include clarity and alignment, focus and prioritization, measurability and accountability, and agility and adaptability.

Continuous Improvement:

Measuring success involves evaluating past performance and driving continuous improvement. By regularly assessing the impact of their Gen AI initiatives, institutions can identify areas for enhancement, refine their strategies, and explore new opportunities for innovation. This iterative approach ensures that Gen AI remains a dynamic and evolving tool that continues to deliver value to students, faculty, and the institution.

Social Justice and Ethical Considerations:

As we pursue the successful adoption and utilization of Gen AI in higher education, it is crucial to consider social justice and ensure that our efforts are rooted in AI’s ethical and responsible use. We must always remember that AI is a human-centered technology designed to serve the needs and well-being of students, faculty, and the broader community. This means:

  1. Ensuring equal access to Gen AI tools and resources, regardless of socioeconomic status, race, gender, or other demographic factors.
  2.  Addressing potential biases in AI algorithms and data sets to prevent the perpetuation of systemic inequalities.
  3.  Protecting student privacy and data security, and being transparent about how data is collected, used, and stored.
  4.  Fostering a culture of responsible AI use, where the limitations and potential risks of the technology are openly discussed and mitigated.
  5.  Engaging in ongoing dialogue with students, faculty, and other stakeholders to understand their needs, concerns, and perspectives on using Gen AI in education.

In human-centered AI, the POST framework, which stands for People, Objective, Strategy, and Technology, must be considered. Introduced by Charlene Li in the book Groundswell, this framework emphasizes the importance of putting people first when adopting new technologies, ensuring that the objectives align with the users’ needs and goals, developing strategies that support the effective implementation and use of the technology, and finally, selecting the appropriate technology to meet these objectives.

By applying the POST framework to adopting Gen AI in higher education, institutions can ensure that their AI initiatives are human-centered and designed to benefit students, faculty, and staff. This means:

  • People: Understanding the needs, preferences, and concerns of the various stakeholders involved in the educational process and designing AI systems that meet their requirements.
  • Objective: Aligning the goals of AI adoption with the overall mission and objectives of the institution, ensuring that the technology enhances teaching, learning, and student success.
  • Strategy: Developing comprehensive plans for the implementation, integration, and governance of AI systems, including training and support for users, data management, and ethical guidelines.
  •  Technology: Selecting the most appropriate AI tools and platforms based on the identified objectives and strategies and ensuring their compatibility with existing systems and infrastructure.

By prioritizing social justice and ethical considerations in our Gen AI initiatives, we can ensure that the technology benefits all members of our educational community and contributes to a more equitable and inclusive future for higher education.

Beyond the Code: Shaping the Future with AI-Integrated Curricula

The inspiration for this blog post was sparked by engaging discussions in the last couple of months as we advance our campus’ AI Community of Practice (COP) initiative. This week, I talked with Professor Matt Beane about the intricacies of working and learning alongside intelligent machines. I also spoke with another professor about the critical importance of embedded ethics within our curriculum. Finally, the questions I raise below are also inspired by conversations with other attendees and listening to the University of California AI Congress panelists, which is currently in progress. These interactions have led me to contemplate two questions at the forefront of our journey into the future of higher education: 

  • How can we equip our students to navigate a life enriched by artificial intelligence in their living, working, and learning environments? 
  • In this rapidly evolving landscape, how do we redefine the pedagogical frameworks of higher education to empower our students as discerning consumers and transformative agents of their futures?
  • How could a major or discipline that promotes competencies prepare our students to live, work, and learn alongside intelligent machines? 

I asked ChatGPT what major could look like. The prompts I use, which resulted in the response below, could be more sophisticated. Still, they will introduce a starting point to answer the questions above.

Technology and Human Society Major

The Technology and Human Society major is an innovative, interdisciplinary program designed to prepare students for the challenges and opportunities of integrating artificial intelligence, robotics, and digital technologies into our daily lives. This four-year curriculum bridges the gap between technology and the liberal arts, focusing on developing a broad set of skills, including technical proficiency, ethical reasoning, and a deep understanding of the societal impact of technological innovation.

Year 1 lays the foundational knowledge in computer science, artificial intelligence, psychology, and ethics, emphasizing the role of technology in society and the ethical considerations accompanying technological advancement.

Year 1: Foundations

Fall Semester

  • Introduction to Artificial Intelligence and Robotics
    • Objective: Introduce students to AI and robotics fundamentals, including history, key technologies, and applications.
    • Content: Overview of AI (machine learning, neural networks), robotics (types, uses in industry), and the societal impacts of these technologies.
    • Activities: Lectures, essential programming assignments, and group discussions on AI’s ethical implications.
  • Introduction to Computer Science
    • Objective: Provide a foundation in computer science principles and programming.
    • Content: Basics of programming (using languages like Python or Java), data structures, algorithms, and software development processes.
    • Activities: Coding exercises, project work to develop simple applications, quizzes.
  • Introduction to Psychology
    • Objective: Offer insights into human behavior, cognition, and how this knowledge applies to technology design and interaction.
    • Content: Cognitive processes, learning theories, motivation, emotion, perception, and human factors in design.
    • Activities: Case studies, experiments, and written assignments on psychology’s role in technology.
  • General Education Requirement (Mathematics)
    • Objective: Strengthen mathematical skills foundational to technical disciplines.
    • Content: Algebra, trigonometry, basic statistics, relevance of mathematical principles in technology and science.
    • Activities: Problem sets, exams, group projects applying mathematical concepts to real-world problems.

Spring Semester

  • Introduction to Ethics in Technology
    • Objective: Explore the ethical challenges in technology development and deployment.
    • Content: Privacy, security, data ethics, AI biases, and ethical frameworks.
    • Activities: Debates, ethical dilemma case studies, reflective essays.
  • Principles of Sociology
    • Objective: Understand the impact of technology on society and vice versa.
    • Content: Social structures, cultural norms, technology’s role in societal change, digital divide issues.
    • Activities: Research papers, group presentations, and discussions on technology’s societal impacts.
  • Calculus for Engineers
    • Objective: Equip students with calculus tools applicable to engineering and technology fields.
    • Content: Limits, differentiation, integration, applications of calculus in problem-solving.
    • Activities: Problem-solving sessions, quizzes, and application projects.
  • General Education Requirement (Writing)
    • Objective: Enhance written communication skills, which are crucial for all professional fields.
    • Content: Academic writing, research paper construction, argumentative essays, technical writing basics.
    • Activities: Writing assignments, peer reviews, workshops on research and citation.

This first year sets a strong foundation across various disciplines, emphasizing the integration of technical skills with an understanding of ethical, psychological, and societal aspects. This holistic approach prepares students to navigate the complexities of technology’s role in society.

Year 2 expands on this foundation with courses in human-computer interaction, digital humanities, environmental science, and data science, encouraging students to explore the interdisciplinary nature of technology’s relationship with human culture and environmental sustainability.

Year 2: Interdisciplinary Exploration

Fall Semester

  • Human-Computer Interaction (HCI)
    • Objective: Explore the design, evaluation, and implementation of interactive computing systems for human use.
    • Content: Principles of HCI, user-centered design, usability testing, and the impact of HCI in developing compelling user interfaces.
    • Activities: Design projects, usability studies, and critiques of existing systems.
  • Environmental Science and Sustainable Technology
    • Objective: Investigate the role of technology in addressing environmental challenges and promoting sustainability.
    • Content: Fundamentals of environmental science, renewable energy technologies, sustainable design principles, and case studies on technological solutions to ecological problems.
    • Activities: Research papers on sustainable technologies group projects designing sustainable solutions.
  • Digital Humanities
    • Objective: Introduce students to the application of digital technologies in humanities research and scholarship.
    • Content: Digital tools and methods in humanities research, digital archiving, text analysis, and the impact of digital technology on cultural artifacts.
    • Activities: Digital project assignments, workshops on digital tools, analysis of digital humanities projects.
  • Elective (e.g., Foreign Language, Creative Arts)
    • Objective: Allow students to explore interests outside their major and enhance their soft skills or global competencies.
    • Content and Activities: Depending on the elective chosen, students might engage in language learning, artistic creation, or other creative pursuits, emphasizing the importance of diverse skills in a technologically driven world.

Spring Semester

  • Data Science Fundamentals
    • Objective: Offer an introduction to the core concepts of data science and its applications.
    • Content: Basic statistics, data analysis, machine learning algorithms, and data visualization techniques.
    • Activities: Data analysis projects using real-world datasets and hands-on exercises with data science tools.
  • Technology Policy and Governance
    • Objective: Examine the complex relationship between technology innovation, policy formulation, and governance mechanisms.
    • Content: Overview of technology law, privacy issues, intellectual property rights, and governance models for emerging technologies.
    • Activities: Policy analysis papers, guest lectures from technology law and policy experts.
  • Introduction to Robotics
    • Objective: Provide foundational knowledge on robotic systems’ design, operation, and application.
    • Content: Basics of robotics, including sensors, actuators, control systems, and robot programming.
    • Activities: Robotics lab exercises, programming assignments, and design of simple robotic systems.
  • Elective (e.g., Business Fundamentals, Philosophy)
    • Objective: Allow students to explore additional disciplines that complement their understanding of technology’s societal role.
    • Content and Activities: Depending on the elective chosen, students could study the basics of business management, ethical philosophy, or other areas that broaden their educational experience.

This year builds on the foundational knowledge acquired in Year 1, expanding students’ understanding of the interaction between technology and various facets of society and the environment. The curriculum is designed to foster an interdisciplinary approach, encouraging students to apply technology in solving complex societal problems while considering ethical, environmental, and policy implications.

Year 3 delves into advanced topics such as cyber-physical systems security, cognitive science, healthcare robotics, and the societal implications of technology, preparing students for the complex ethical and practical challenges they will face in the tech-driven world.

Year 3: Advanced Topics and Applications

Fall Semester

  • Cyber-Physical Systems Security
    • Objective: Understand the security challenges and strategies of cyber-physical systems, which integrate physical processes with networked computing.
    • Content: Principles of cybersecurity, vulnerabilities of cyber-physical systems, security technologies, and case studies on securing infrastructure.
    • Activities: Simulations of cyber-attacks, design of security solutions, analysis of recent cybersecurity incidents.
  • Cognitive Science and Artificial Intelligence
    • Objective: Explore the intersection of cognitive science and AI, focusing on how AI models can replicate or augment human mental processes.
    • Content: Basics of cognitive science, neural networks, natural language processing, and cognitive robotics.
    • Activities: Projects developing simple AI models, discussions on AI and cognition, and critiques of AI’s role in understanding the human mind.
  • Robotics in Healthcare
    • Objective: Examine the application and implications of robotics in healthcare, including surgery, rehabilitation, and patient care.
    • Content: Types of healthcare robots, ethical considerations, patient safety, and robotics case studies in clinical settings.
    • Activities include evaluating robotic healthcare technologies, guest lectures from healthcare professionals, and designing proposals for new healthcare robotics applications.
  • Elective (e.g., Advanced Programming, Machine Learning)
    • Objective: Provide in-depth technical skills relevant to the student’s interests and career goals.
    • Content and Activities: Depending on the elective, students could engage in advanced software development projects, machine learning model building, or other specialized technical tasks.

Spring Semester

  • Technology and Society
    • Objective: Delve into the complex relationship between technology and societal development, focusing on historical and contemporary perspectives.
    • Content: Technology’s role in social change, digital culture, technology and inequality, and future predictions.
    • Activities: Research papers on technology’s societal impacts, seminars with technology thought leaders and collaborative group projects.
  • Ethical AI
    • Objective: Address the ethical dimensions of AI development and use, including bias, transparency, and accountability.
    • Content: Ethical frameworks for AI, case studies of AI ethics in practice, regulation and policy implications.
    • Activities: Ethical audits of AI systems, debates on AI ethics topics, and development of ethical guidelines for AI projects.
  • Project-Based Learning in AI and Robotics
    • Objective: Apply knowledge and skills in AI and robotics to a real-world or simulated project.
    • Content: Project management, teamwork, technical development, and project presentation.
    • Activities: Team projects from conception to demonstration, project reports, presentations to peers and faculty.
  • Elective (e.g., Innovation and Entrepreneurship, Advanced Data Science)
    • Objective: Expand students’ abilities to innovate and apply data science techniques in various contexts.
    • Content and Activities: Depending on the elective chosen, students could create a startup business plan, engage in advanced statistical analysis, or explore innovative technology solutions.

This third year is crucial for deepening students’ technical expertise and understanding of the broader implications of technology. It emphasizes applying theoretical knowledge to practical and ethical challenges, preparing students for advanced study, research, or professional careers in technology and society.

Year 4 culminates in a capstone project that integrates the knowledge and skills acquired throughout the program alongside courses in professional development and electives that allow for specialization in areas of personal interest.

Year 4: Specialization and Integration

Fall Semester

  • Capstone Project I
    • Objective: Begin a comprehensive project integrating knowledge and skills acquired throughout the major. The project should address a significant issue at the intersection of technology, society, and ethics.
    • Content: Project proposal development, literature review, project planning, and initial implementation.
    • Activities: Weekly project meetings, progress presentations, peer feedback sessions.
  • Seminar on Current Topics in Technology and Society
    • Objective: Engage with cutting-edge discussions on the impacts of technology on society, including emerging trends and challenges.
    • Content: Guest lectures, current articles and case studies, and student-led seminars on topics of interest.
    • Activities: Participate in discussions, present seminar topics, and write reflective essays.
  • Elective in Area of Specialization (e.g., Advanced Robotics, AI in Finance)
    • Objective: Deepen technical knowledge and skills in a specific area of interest related to technology and society.
    • Content and Activities: Depending on the elective, coursework could involve advanced technical training, project work, and industry or research applications.
  • Elective (General Education or Free Elective)
    • Objective: Offer a final opportunity to explore interests outside the major or to complement the major with additional skills or knowledge.
    • Content and Activities: Varied, depending on the student’s interests and the offerings available, such as arts, humanities, social sciences, or additional technical electives.

Spring Semester

  • Capstone Project II
    • Objective: Complete and present the capstone project, demonstrating the integration of technical skills, ethical considerations, and societal impact.
    • Content: Final implementation, analysis, and evaluation of the project results.
    • Activities: Public project presentation, final report submission, peer and faculty feedback.
  • Professional Development in Technology
    • Objective: Prepare for career success in the technology sector, including job search strategies, professional networking, and life-long learning skills.
    • Content: Resume building, interview skills, professional ethics, continuing education opportunities.
    • Activities: Workshops, mock interviews, networking events, alum panels.
  • Elective in Area of Specialization (e.g., Ethical Hacking, Digital Marketing)
    • Objective: Continue to build expertise in a chosen specialization, preparing for specific career paths or advanced study.
    • Content and Activities: Advanced coursework and projects tailored to the specialization, potentially including certifications, competitions, or collaborations with industry partners.
  • Elective (General Education or Free Elective)
    • Objective: Complete the undergraduate experience with a course that broadens perspectives or enhances personal and professional skills.
    • Content and Activities: Options could include advanced study in a foreign language, leadership development, creative arts, or other areas of personal interest.

The final year is designed to culminate the interdisciplinary learning experience, with a significant focus on the capstone project that embodies the student’s understanding and application of technology in society. It also emphasizes professional development and specialization, preparing students to transition from academic study to career or further education. This curriculum equips graduates with a comprehensive skill set that is both technically proficient and ethically aware, ready to tackle the challenges of a rapidly evolving technological landscape.

Graduates of the Technology and Human Society major will be uniquely equipped to contribute to various fields, from technology development and policy to digital humanities and environmental sustainability. They will possess the critical thinking, ethical reasoning, and technical skills necessary to navigate and shape the future of our increasingly digital world.

AI’s role in higher education is inevitable; it’s already integrated into all aspects of the campus, from teaching and learning to research and administrative operations. What is not inevitable is the utility and effectiveness of AI in the future world where our students will live, learn, and work. Higher education must consider the following questions to prepare students to live, work, and learn effectively in AI.

  • How can we equip our students to navigate a life enriched by artificial intelligence in their living, working, and learning environments? 
  • In this rapidly evolving landscape, how do we redefine the pedagogical frameworks of higher education to empower our students as discerning consumers and transformative agents of their futures?
  • How could a major or discipline that promotes competencies prepare our students to live, work, and learn alongside intelligent machines? 

Charting the Future of AI in Higher Education: An Invitation to Collaborate on a Higher Education AI Capability Maturity Model

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.

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