Why Some Academics Resist AI Even While Using It Daily

When the 'AI Tools' promise productivity but deliver headaches.Image generated by Gemini (Google).

Artificial intelligence has created new opportunities in teaching, research, and cognitive growth. Many scholars embrace it with open minds and see the possibilities beyond chatbots. Yet a noticeably vocal segment still rejects AI at every turn, even as they use it for emails, planning, literature reviews, and course design.

This contradiction raises a simple but honest question: Why do some academics resist AI while quietly relying on it?

From my experience in higher education, mentoring, and instructional design, the issue runs deeper than technology. It touches identity, control, ego, and the comfort of familiar intellectual rituals.

Below is a clear explanation of the patterns that I see in academic spaces without sugarcoating any part of it.

The Resistance Is Not Universal, but It Is Predictable

A growing percentage of higher education uses AI for:

• curriculum development

• grading support

• simulation-based learning

• accessibility and differentiation

• research coding

• conceptual modeling

Those who resist tend to come from areas where traditional epistemologies define professional identity. They built reputations on older methods, and those methods once gave them unmatched authority in classrooms and committees.

AI rearranges that hierarchy.

The issue is not incompetence.

The issue is attachment.

Being “Anti-AI” Becomes a Persona, Not a Position

Some academics prefer to be known as anti-something rather than careful skeptics. Their stance becomes a type of identity, a performance that signals authority:

• “I’m the one who spots the flaws.”

• “I stand in defense of real scholarship.”

• “These tools are beneath advanced thinkers.”

This performance is attractive because it restores a sense of intellectual importance. It also gives them comfort in their expertise, even if they rarely engage with the full system they critique.

They do not present full analysis.

They present fragments.

They pick one issue and use it as the basis for portraying the entire technology as empty. That is not skepticism. That is selective denial.

Constructive Skepticism Builds Understanding — Denial Stops It

There is nothing unsafe about skepticism. In fact, it is essential.

A constructive skeptic asks:

• “What works?”

• “What needs refinement?”

• “How do we measure impact?”

• “What are the cognitive strengths and limits?”

This approach advances knowledge.

But what one often encounters isn’t skepticism it is flat denial with no real engagement. These individuals speak in sweeping conclusions based on small experiences, shallow tests, and outdated assumptions.

They claim to be defenders of integrity, yet they practice cognitive dissonance: teaching curiosity while modeling resistance to new learning.

Flawed Evaluation Becomes a Tool for False Certainty

A common pattern appears in anti-AI academic critiques: the methodology is biased from the start.

Examples:

• Using slang or vague phrasing as the input

• Expecting graduate-level responses

• Switching variables while pretending the constant remained untouched

• Judging an output built on weak input

• Comparing a simple prompt to a doctoral argument and calling it a “failure”

This is the academic version of:

Asking someone with a third-grade vocabulary to speak…then grading them with a graduate-level rubric…and using that mismatch to announce that literacy is falling.

The problem is the process, not the person.

The problem is the prompt, not the platform.

When academics ignore this, the conclusion becomes performative instead of rational.

Academic Arrogance Masks Shallow Understanding

Some scholars rely heavily on:

• tenure

• titles

• seniority

• degrees

• old methods

• reputation within their circle

These credentials once guaranteed authority. AI weakens that guarantee because students now have access to knowledge without needing permission or proximity.

In response, resistance becomes a shield:

• dismissing counterarguments

• ignoring new research

• hiding behind jargon

• shutting down dialogue with rhetoric

• gaslighting students or colleagues who raise valid points

This is not the spirit of academic growth.It is the instinct of control.

AI Changes the Structure of Academic Power

AI decentralizes knowledge.It moves authority away from:

• gatekeepers

• credential-only hierarchies

• departments that thrive on exclusivity

• scholars who rely on difficult language to maintain status

It empowers:

• autodidacts

• learners at all levels

• practitioners outside academia

• students who prefer individualized learning

• communities that were once excluded by tradition

This shift challenges people who built their identity on being the only source of expertise in the room.

Self-Directed Learning Is Gaining Momentum

Students now use AI to:

• tailor study plans to their pace

• build personalized scaffolding

• simulate conversations and scenarios

• redesign concepts until they understand them

• learn at multiple difficulty levels

• receive real-time feedback

• practice communication skills

• challenge material through multiple angles

This supports heutagogy — the idea that learners take responsibility for their own growth.

In this model, instructors guide, support, review, and refine. They do not control the flow of information.

This shift is uncomfortable for those who believe teaching is defined by audience dependence rather than audience empowerment.

Selective Anti-Teaching Weakens Intellectual Integrity

This critical issue is worth stating clearly:

Some academics amplify one weakness of AI and use it to dismiss the entire tool.

This approach:

• discourages exploration

• blocks cognitive expansion

• reinforces stagnant thinking

• undermines student agency

• distorts the purpose of scholarship

• stops meaningful progress

When educators behave like this, they contradict the values they claim to uphold. They teach students how to resist thought instead of how to refine thought.

A Clear Takeaway

The problem is not AI.

The problem is the discomfort, ego, and uncertainty AI exposes.

Certain academics resist AI because:

• It challenges their authority

• It creates new forms of competence

• It reveals bias they once ignored

• It supports learners who struggled under traditional models

• It reduces dependence on academic gatekeeping

• It empowers voices often overlooked

• It shifts how knowledge is built and shared

They prefer to be seen as critics rather than thinkers.

They cling to the identity of resistance instead of the discipline of inquiry.

And in the process, they forget what education is supposed to be.

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