Why AI Isn't Good or Bad (And Why That Matters)

Episode Overview

Artificial Intelligence is transforming higher education at an unprecedented pace. While many conversations focus on AI tools themselves, a deeper question remains: How should educational institutions think about AI as a social force?

In this episode of Learning with AI, host Gladys Mercier sits down with Professor Marx Gomez-Liendo, sociologist at De Anza College, to explore how sociology offers a unique lens for understanding AI's impact on education, knowledge production, and institutional change.

Together, they discuss open education, AI literacy, equity, and why the future of AI in higher education depends not only on technology—but on the people and policies shaping its adoption.

A Sociological Perspective on AI

Unlike many conversations centered on technical capabilities, Professor Gomez-Liendo approaches AI from the perspective of sociology—the study of how societies, institutions, and human relationships evolve.

Rather than asking whether AI is inherently good or bad, he encourages educators to examine the broader systems surrounding the technology.

From this perspective, AI is not simply another educational tool. It is a social force capable of accelerating existing institutional dynamics, raising important questions about equity, governance, participation, and educational responsibility.

"AI isn't inherently good or bad. It depends on the conversations we have and the policies we design."

AI as a Catalyst for Educational Change

One of the central ideas throughout the conversation is the notion that artificial intelligence functions as a catalyst.

Rather than creating entirely new opportunities or problems, AI often amplifies what already exists within educational systems.

Institutions that prioritize accessibility, collaboration, and inclusive learning may find AI accelerating those efforts. At the same time, existing inequalities, biases, or structural barriers can also become more visible—and potentially more pronounced.

This perspective shifts the conversation away from simply adopting AI tools toward designing thoughtful educational systems capable of using those tools responsibly.

Open Education Beyond Access

Professor Gomez-Liendo also discusses his research on open education, describing it as more than providing free educational resources.

For him, open education represents a broader movement centered on equity, social justice, and removing barriers to learning.

The conversation explores how AI presents both opportunities and risks for this movement.

AI can improve accessibility through translation, adaptive support, and content generation, but it also raises important concerns around representation, intellectual property, and whose knowledge becomes most visible within educational environments.

Rather than assuming AI automatically democratizes education, institutions must remain intentional about the values embedded in their learning experiences.

Learning Together with AI

Across higher education, faculty are actively experimenting with AI in their classrooms.

Professor Gomez-Liendo describes what he calls an emerging culture of experimental open pedagogy, where instructors and students are learning together as AI continues to evolve.

Instead of treating AI as either something to prohibit or fully embrace, many educators are designing activities that encourage students to critically examine AI-generated content, identify potential biases, and preserve their own authentic voice while using AI as a learning support.

These experiments represent an important shift toward collaborative learning rather than technology-driven instruction.

AI Literacy Requires More Than Using AI

The conversation emphasizes that successful AI adoption goes far beyond providing access to AI tools.

Developing meaningful AI literacy also requires understanding:

• Data privacy
• Algorithmic bias
• Security and governance
• Civic responsibility
• Ethical decision-making

Rather than focusing exclusively on technical proficiency, institutions should help students and faculty develop the critical thinking skills necessary to navigate AI responsibly.

Supporting Faculty Through Collaboration

As colleges and universities develop AI strategies, Professor Gomez-Liendo argues that successful implementation depends on participation rather than top-down decision making.

Faculty, students, administrators, and community members all bring valuable perspectives that should inform institutional AI policies.

Creating opportunities for dialogue not only improves decision making but also helps build trust across campus communities during periods of rapid technological change.

According to Professor Gomez-Liendo, institutions that actively listen to diverse voices are better positioned to adopt AI in ways that reflect their educational mission and values.

Preserving Human Agency

The episode concludes with a powerful reminder that technology should never replace human responsibility.

Although AI is already influencing education, healthcare, government, and countless other sectors, people still retain the ability—and the obligation—to shape how these technologies evolve.

Rather than allowing technology to dictate educational practice, institutions have an opportunity to intentionally design systems that reflect human values, academic integrity, and meaningful learning.

As Professor Gomez-Liendo reminds us:

"We created these technologies. They don't create us."

Key Takeaways

• AI should be viewed as a catalyst rather than inherently good or bad.
• Sociology provides valuable insight into how AI reshapes educational systems.
• Open education requires intentional attention to equity, accessibility, and representation.
• AI literacy extends beyond tool usage to include ethics, governance, privacy, and critical thinking.
• Successful institutional AI adoption depends on collaboration, participation, and trust.
• Universities have the opportunity—and responsibility—to shape AI in ways that support human learning rather than simply adapting to technological change.

Episode Transcription

Intro: Welcome to Learning with AI, a ScholarStack podcast. In each episode we explore how artificial intelligence is changing the way we learn, teach and think. Through conversations with educators, researchers, students and academic leaders, we discuss the ideas and challenges shaping the future of learning.

At ScholarStack we believe AI doesn't replace learning, it enhances it.

AI isn't just changing education, it's changing society. Today professor Marx Gomez-Liendo shares why the most important questions about AI aren't just technological, but human. We hope you enjoy this conversation. Let's get started.

Gladys Mercier: Welcome to Learning with AI, a podcast from ScholarStack. We bring together voices from across the academic world to explore how AI is changing the way we teach, learn, lead, and think. No hype, no easy answers—just honest conversations about a complicated moment.

I'm your host, Gladys Mercier, and today I'm joined by Professor Marx Gomez-Liendo. Marx is a sociology professor at De Anza College.

Welcome, Marx.

To begin, it might be helpful for our audience if you could tell us: what does a sociological perspective bring to conversations like this?

Marx Gomez-Liendo: Thank you, Gladys. I'm really excited to be part of this podcast.

Simply put, sociology is the study of human interactions at different scales. We examine everything from small groups and communities to entire societies and global issues. We use different theoretical perspectives and research methods to better understand how people interact and how those interactions shape our world.

One of sociology's strengths is connecting what often appears to be an individual problem to a much larger social context. For example, unemployment isn't only a personal issue—it is also connected to economic policies. Likewise, AI isn't just a technological innovation; it's connected to broader changes in education, government, and society.

That's the perspective sociology brings to conversations like this.

Gladys Mercier: Excellent. Thank you.

What first sparked your interest in sociology, and what areas are you researching today?

Marx Gomez-Liendo: It's actually a story that goes back to my childhood. My father studied political science, and my parents named me Marx after Karl Marx.

When I was transitioning from high school to college, I became curious about my name and started reading Karl Marx's work. Through that process, I discovered sociology, and I immediately connected with it.

I've always been someone who enjoys observing people and the world around me. Sociology requires curiosity, analytical thinking, and imagination to make connections between individual experiences and larger social structures.

I often tell my students that I fell in love with sociology during my very first lecture.

We were watching Modern Times by Charlie Chaplin, and something just clicked for me.

As for my research, I've worked on a variety of topics over the years. As an undergraduate, I completed an honors thesis examining copyright and its impact on education and culture. Although I'm not a lawyer, I wanted to understand how copyright policies influence access to knowledge.

That research introduced me to remarkable scholars and activists in my home country of Venezuela. Together, we even formed a small collective called Communalize Knowledge, which advocated for conversations around open education.

Before moving to the United States, I also worked as a policy analyst and later became a research associate at a scientific institute in Venezuela, focusing on environmental sociology and political ecology.

More recently, my work has explored open education and its relationship with decolonial theory. Alongside that, I continue researching topics such as energy transitions and mining conflicts in Latin America.

Although those subjects may seem different, they're connected by one common goal: bringing multiple perspectives into complex conversations. I believe that's part of what I call cognitive justice—making different ways of knowing visible when addressing complicated issues.

Gladys Mercier: Wow—that's quite a journey. You've covered an incredible range of topics throughout your career.

Since our audience is especially interested in education, let's spend a little more time on open education.

How do you define it, and what are its primary goals?

Marx Gomez-Liendo: Open education can mean many different things. It can be a field of research, a policy discussion, or a collection of educational practices.

Professionally, I see it as a social and pedagogical movement.

At its core, it's rooted in principles such as equity, social justice, reducing barriers to learning, and creating more democratic educational systems.

But one interesting question naturally follows.

If we're advocating for open education, does that imply that education can also be closed?

And if so, in what ways is education inaccessible or closed off to certain people?

Those are the kinds of questions that motivate much of the work within the open education movement.

Gladys Mercier: I know you recently wrote a paper on this topic that's also being published as a book chapter.

Could you tell us a little about that work?

Marx Gomez-Liendo: Absolutely.

In that paper, I argue that open education currently stands at a crossroads.

It has the potential either to reinforce what I call colonial biases or to become an opportunity for genuine decolonial transformation.

I think of open education as having this dual nature.

If we return to its guiding principles—accessibility, equity, and social justice—we quickly realize that inaccessible learning materials create closed educational experiences for many people.

In my own education, for example, I barely encountered Venezuelan or Latin American sociologists until my senior year of college.

That, too, represents a closed educational experience—not because the information doesn't exist, but because the curriculum doesn't make it visible.

So one risk is that open education unintentionally reinforces existing hierarchies.

On the other hand, it also creates opportunities to broaden the conversation by bringing in more perspectives—not only through greater student diversity, but also through faculty diversity, curriculum design, learning materials, and the relationships we build inside educational environments.

We have the opportunity to move away from traditional top-down approaches and toward more collaborative, dialogical models of learning.

For that reason, I don't assume that open education is automatically emancipatory.

I think we have to continue asking these difficult questions if we want the movement to fulfill its transformative potential.

Gladys Mercier: Right. As a sociologist, you study how societies evolve when they encounter new movements and new technologies.

Now we have artificial intelligence—arguably one of the most powerful technologies humanity has ever created—which is already becoming a major social force.

Before we focus specifically on education, I'd love to hear your broader perspective.

What excites you about AI, and what concerns you most about its impact on society?

Marx Gomez-Liendo: I would describe myself as cautiously optimistic when it comes to AI.

I don't think it's helpful to approach the conversation from a purely pessimistic perspective. At the same time, history reminds us that technological advances don't automatically reduce inequality. In many cases, they can actually reinforce existing disparities.

What interests me most is everything that exists between those two extremes.

There are people who absolutely love AI and people who strongly oppose it. I'm interested in the space between those positions, because that's where questions about policy, regulation, organizations, and collective decision-making emerge.

As a sociologist, I'm also deeply interested in context.

Technology is never created in a social vacuum. Every technological innovation is developed within a particular cultural, political, and social environment.

Today we live in an increasingly accelerated world. We're expected to do more work, accomplish it faster, and continuously increase productivity.

Then AI arrives almost like a miracle—promising to help us accomplish even more in even less time.

The question is whether those efficiency gains will actually improve our quality of life.

Will they give us more balance?

Or will they simply result in even more work, even greater expectations, and eventually AI-driven burnout?

Those are the questions that interest me.

As sociologists, we have a responsibility to introduce nuance into this conversation—to move beyond both the hype and the fear.

Neither "AI will solve everything" nor "AI should never exist" provides enough guidance for policymakers or educational institutions.

Gladys Mercier: Exactly.

We often fall into those two extremes. People either love AI or hate it. They're either afraid of it or convinced it's going to solve every problem overnight.

That's just human nature.

So let's connect these ideas.

How do you see the intersection between AI and open education?

Marx Gomez-Liendo: Earlier we talked about open education existing at a crossroads.

When I think about AI in that context, one metaphor immediately comes to mind.

I see AI as a catalyst.

It has the potential to amplify colonial biases, but it also has the potential to accelerate decolonial transformation within educational institutions.

There are many positive possibilities.

For example, AI can significantly improve accessibility.

Imagine an instructor creating a complex diagram or image. AI can quickly generate accessibility descriptions that support screen readers, making learning materials available to more students.

Translation is another example.

For students whose first language isn't English, AI can help make educational materials more accessible.

Of course, human review is still essential.

Even before AI, things were often lost in translation. AI doesn't eliminate that challenge—it simply changes it.

At the same time, AI introduces important questions around privacy, data ownership, market dynamics, and the concentration of power among technology companies.

That's why I continue to think of AI as a catalyst.

It can certainly accelerate the creation of open educational resources, although copyright and intellectual property remain important considerations.

Ultimately, I don't believe AI is inherently good or inherently bad.

Its impact depends on the conversations we have, the policies we design, and the way we choose to govern its use.

Gladys Mercier: That's a really helpful way to think about it.

What are you seeing among your colleagues today?

How are faculty members responding to AI, and what kinds of approaches are emerging in higher education?

Marx Gomez-Liendo: That's a fascinating question because I've noticed something happening across many disciplines.

I don't know that we've fully named it yet, but I see what I would call experimental open pedagogy.

We're all learning with AI—just like the title of this podcast suggests.

Professors are learning.

Students are learning.

Administrators are learning.

Everyone is trying to understand what this technology means.

I've seen instructors designing assignments where students learn alongside AI—exploring both its strengths and its limitations.

These experiences are creating exciting opportunities for open pedagogy, where students and instructors collaborate to build learning materials, assignments, and even parts of the curriculum together.

That changes knowledge production.

Instead of knowledge flowing only from the expert to the learner, it becomes something a learning community creates together.

I've also seen faculty in writing courses emphasizing the importance of maintaining students' authentic voices.

AI can absolutely be useful for proofreading.

But when AI begins changing the tone or the identity of someone's writing, that's when we start encountering real concerns.

The risk is that our writing—and eventually our thinking—becomes standardized.

I've also seen instructors asking students to generate AI-created images and then critically analyze the biases embedded within those images.

To me, that's incredibly promising.

Regardless of whether individual faculty members are enthusiastic about AI or skeptical of it, there's an ethical responsibility to engage with these technologies in meaningful ways.

Of course, as a researcher, I'd love to see more systematic studies examining which approaches truly improve learning outcomes.

But it's encouraging to see so many educators experimenting and sharing new ideas.

Gladys Mercier: What does that experimentation look like in your own classroom?

Marx Gomez-Liendo: My approach has been fairly simple.

I conduct focus groups with my students.

We talk openly about AI.

I ask about their concerns, their professional goals, and the kinds of support they'd like to receive from the institution.

As a sociologist, I'm fundamentally interested in listening to people.

Focus groups give students the opportunity to share their experiences, and they help me better understand how they're navigating these technologies.

Gladys Mercier: Are you using AI itself to support those conversations?

Marx Gomez-Liendo: Yes, although probably not in the way people might expect.

I don't use AI to conduct the focus groups.

Instead, I use a locally installed AI transcription tool to transcribe the conversations afterward.

I appreciate that approach because everything stays on my own computer. Student voice data isn't uploaded to the cloud, which helps address important privacy concerns.

Looking ahead, I'd also like to begin experimenting with AI tools that support academic research.

Today there are tools that can assist with literature reviews and help researchers navigate large collections of academic publications much more efficiently.

I'd like to explore those tools with my qualitative research methods class next academic year.

Hopefully I'll have some interesting experiences to share the next time we talk.

Gladys Mercier: I hope so, too.

It sounds like we'll have to invite you back after you've had a chance to experiment with those approaches.

One thing that strikes me is that good AI habits can actually be taught—but they need to be intentionally built into the classroom experience so students learn how to use AI thoughtfully instead of simply asking it to do the work for them.

Marx Gomez-Liendo: Exactly.

Gladys Mercier: Before we get to my final question, I'd like to go back to your sociology perspective for a moment.

When you think about the AI enthusiast community—and by that I mean the people pushing AI adoption as quickly as possible—what do you think they could learn from sociological thinking? How can we get this right, particularly in education?

Marx Gomez-Liendo: I think the biggest lesson is the importance of creating collaborative spaces and participatory processes whenever institutions develop AI initiatives or policies.

Enthusiasm is valuable because it moves innovation forward. But when organizations fail to recognize that people have different perspectives, different concerns, and different levels of comfort with AI, they risk creating resistance.

They risk creating mistrust.

Educational institutions need spaces where those conversations can happen openly.

That might mean interdisciplinary discussions among faculty.

It might mean student forums where learners can share their experiences and concerns.

For community colleges, it might even involve outreach to the communities they serve.

Those kinds of participatory frameworks don't slow innovation—they strengthen it.

They allow institutions to identify important perspectives that might otherwise be overlooked.

Without those conversations, we simply don't know what we're missing.

From my own research, I would describe that as a decolonial imperative.

We should always be asking:

Who has been left out of this conversation?

How can we bring them into it?

How can we create spaces where every voice has an opportunity to be heard?

That would be my methodological advice to anyone enthusiastic about AI.

We need to open the room to more voices.

Gladys Mercier: I love that.

One of the goals of this podcast is exactly that—to bring more voices into the conversation and create opportunities for dialogue.

So for my final question...

Thinking about the kinds of learning communities we've been discussing, how can universities and institutional leaders better support faculty members who genuinely want to use AI in meaningful ways?

Marx Gomez-Liendo: I think the first step is recognizing that support shouldn't focus exclusively on using AI tools.

It should also focus on understanding the broader implications of using them.

This is very similar to the conversation around open education.

It's never just about access.

It's also about knowledge production, power dynamics, and how learning takes place.

Providing professors or students with a premium license for an AI tool isn't enough.

Institutions need to help their communities develop a much deeper understanding of AI literacy.

That includes understanding privacy.

Security.

Algorithmic bias.

And what I would call civic literacy—the ability to think critically about the role these technologies play in society.

One framework that I find particularly useful comes from the California Community Colleges Chancellor's Office.

They developed what they call the HUMANS Framework.

It's not the only approach, but I think it's an excellent example of human-centered AI governance.

The framework emphasizes ideas such as maintaining human support for students, allowing people to opt out of AI-supported experiences when appropriate, protecting privacy, being transparent about how data is collected and used, safeguarding against algorithmic discrimination, and continuously monitoring outcomes to ensure AI is serving learners effectively.

Those principles encourage institutions to look beyond simply adopting technology.

Instead, they focus on building responsible educational ecosystems around AI.

Gladys Mercier: That really shifts the conversation from technology to institutional responsibility.

Marx Gomez-Liendo: Exactly.

Every institution has different circumstances.

Funding is different.

Infrastructure is different.

Student populations are different.

But having a guiding framework allows institutions to adapt AI responsibly within their own context.

And perhaps most importantly, we need to recognize that none of us has all the answers.

It's unrealistic to expect institutional leaders to already know exactly how AI should be implemented.

We're all learning with AI.

That means faculty, students, administrators, and leadership all need opportunities to co-design these initiatives together.

To learn together.

To monitor what works.

To improve what doesn't.

For me, that's what meaningful institutional support really looks like.

Gladys Mercier: That's fantastic.

We have just a couple of minutes left.

Before we wrap up, are there any final thoughts you'd like to leave our audience with?

Gladys Mercier: That's fantastic.

We have just a couple of minutes left. Before we wrap up, are there any final thoughts you'd like to leave our audience with?

Marx Gomez-Liendo: First of all, thank you. I've really enjoyed this conversation, and I appreciate the thoughtful questions.

If I could leave listeners with one final reflection, it would be a call to reclaim our agency.

Sometimes these technologies feel so overwhelming that it seems as though they are making decisions for us.

But we shouldn't forget something very important.

Technology was created by people.

And because it was created by people, people still have the ability—and the responsibility—to shape it.

It's a process of mutual influence.

Yes, AI is already shaping education, healthcare, government, politics, and many other aspects of society.

But we also have the power to shape AI in return.

That's why I encourage people to create spaces for these conversations.

Whether it's a podcast like this one, an open forum at a university, or discussions within departments and institutions, we need opportunities to think collectively about how these technologies should evolve.

Higher education institutions, in particular, have an important responsibility.

They should develop frameworks that allow them to maintain meaningful oversight of the AI technologies being introduced on campus.

That includes thinking carefully about security, privacy, governance, and the specific needs of their own communities.

It shouldn't be a situation where technology companies make all the decisions while educational institutions simply adapt.

Universities need to understand the technologies they're adopting—their strengths, their limitations, and how they can be redesigned or implemented in ways that better serve their students and faculty.

Ultimately, that's the message I'd like to leave people with.

Our agency matters.

We created these technologies.

They didn't create us.

And we should never lose sight of that.

Gladys Mercier: That's a wonderful way to end.

Thank you, Marx, for joining us today.

And thank you to everyone watching and listening.

The references mentioned during today's conversation will be available alongside this episode.

Learning with AI is a production of ScholarStack, offering AI-native infrastructure for education built on learning science and powered by artificial intelligence.

Thank you for joining us, and we'll see you next time.

Outro: Thank you for this episode of Learning with AI, a ScholarStack podcast. We created this space for educators, students, researchers and academic leaders to explore how AI is shaping the future of learning. To continue the conversation follow Scholar Stack on LinkedIn and visit scholarstackai.com for more resources and insights.

If there's a question you want us to explore or a gues you'd like to hear from, let us know. The future of learning is something we're building together.

Thanks for listening and we'll see you in the next episode.