Tag: AI

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.

Transforming Higher Education: How AI and Skilled Educators Can Shape the Future of Learning

I once taught first-year international students an introduction to a university course. Several students mentioned they recorded their lectures because it was a challenge for them to follow their instructors with the language barrier and the instructors speaking style. They would then review their recordings after the class. With this method, students can focus on the instructor with their heads up instead of having their heads down to take notes thereby missing some visual and oral cues from the professor when they’re emphasizing essential concepts.

An instructional designer told me that seats behind the third row in a big lecture hall may as well be considered distance learning. The students who engage by asking questions and participating in discussions are few and often seated in front of the class. During the pandemic, the same instructional designer also noted that Zoom sessions might appear more intimate for students because they can see the instructor up close, including their facial expressions, rather than a minuscule figure in front of an auditorium.

The thoughts above made me think ChatGPT and other generative AI tools could enhance learning and drive transformative changes to higher education. Here are some possibilities:

Flipped classroom. Just like the first example I provided above, AI tools that summarize materials, and introduce concepts in different modes beyond lecture format that may resonate more with the student’s learning styles, can prepare students for active discussions in the classroom. Instructors can provide asynchronous materials (videos, etc.) that students can study before class, and the instructors can then use the class sessions for interactive discussions.

Guide on the side instead of the sage on the stage. Related to the idea above, if higher education is concerned about ChatGPT being used as a cheating device or leading students to become lazy/disengaged, the role of instructors could shift from someone who lectures on stage to an active facilitator. When I attended courses at the UC Berkeley Haas Business School for my executive leadership program and a leadership academy, I was in awe of the caliber of the faculty members. They were experts in their respective fields and skilled facilitators who fostered an engaging and collaborative learning environment. Using the Socratic method, they solicited students’ ideas through dialogues instead of monologues, which was a refreshing experience for a student like me.

Personalized learning. Generative AI as a feedback mechanism can help bridge the gap for students who may be struggling with language barriers and enhance the overall learning experience for all students by addressing their unique strengths and areas for growth. AI tools like ChatGPT can enable personalized learning experiences by providing students with targeted resources and customized feedback based on their learning needs and preferences.

Enhanced accessibility. AI-powered transcription and translation services can make course content more accessible for international students, students with disabilities, or those who prefer learning through different modalities. For example, universities can provide recorded lectures transcribed and translated into other languages, and visual aids can describe visual objects for students with visual impairments in detail, similar to ChatGPT for Be My Eyes.

Virtual mentorship and coaching. Universities can use AI tools like ChatGPT to provide personalized guidance, mentorship, and coaching to students outside the classroom. Students can access on-demand support, helping them navigate academic and personal challenges and improving their overall university experience. An application of this concept is to provide students with self-service applications that can “nudge” or remind them to take certain actions based on information available in student information, learning management systems, or other university systems.

Data-driven teaching and learning. AI tools can analyze vast amounts of student performance and engagement data, allowing instructors to make real-time data-driven decisions or to have better information about their students. One example of this concept is a dashboard for faculty that includes information about their students, including their socioeconomic status, demographic backgrounds, and academic performance in their current or previous courses related to or prerequisites to their class. Along with the data are suggestions for adjusting their courses to improve their students’ performance. The use of generative AI in this way can help faculty members identify struggling students early on, provide targeted support, and continuously improve their teaching strategies and course content.

By adopting the concepts above and integrating AI tools like ChatGPT into higher education, institutions can create more engaging, inclusive, and effective learning environments that enhance student success and better prepare students for the dynamic world they will enter upon graduation. In this new paradigm, faculty members will continue to play a crucial role, not as the sole distributors of knowledge but as skilled facilitators and guides who support and empower students on their learning journeys.

The Case Against Generative AI in Higher Education: 100 Arguments

The debate around incorporating generative AI in higher education is a hot topic among educators, scholars, practitioners, and other interested parties. It’s essential to explore why generative AI may not be the ideal end-all solution for higher education. While there are opportunities and potential advantages of AI-driven learning, such as enhanced efficiency, personalization, and accessibility, we must also consider the possible risks and unintended consequences that come with it.

Below are 100 arguments that reveal the intricate nature of the issue, touching upon ethical dilemmas, pedagogical impacts, the erosion of human agency, and threats to academic freedom. By sharing these insights I hope to encourage a more balanced and informed discussion on the role of Generative AI in shaping the future of higher education. These are generated by ChatGPT, so please offer your critique to the 100 items below.

The Case Against Generative AI in Higher Education: 100 Arguments

The Many Applications of Generative AI, Beyond Student Learning and Teaching

Generative AI, including ChatGPT, is a technology that can potentially transform higher education across all areas of campus operations. Using the Higher Education Reference Model, which outlines the core capabilities of learning and teaching, research, and enabling capabilities, this document, Higher Education Reference Model (HERM) Capability Model & Generative Artificial Intelligence provides ideas on how Generative AI can be applied in higher education.

While generative AI has already proven its potential to revolutionize/disrupt the student learning experience, it can streamline admissions, automate administrative tasks, and support facilities management and IT support. Additionally, AI can help researchers make sense of large amounts of data, identifying patterns and trends and generating new hypotheses.

Ensuring ethical and transparent use of AI is vital. As professionals in higher education, we must continue to explore the various applications of generative AI and its potential to transform higher education beyond the classroom.

Source: https://library.educause.edu/resources/2021/9/the-higher-education-reference-models

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