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University teachers in Palestine said AI aids creativity but weakens effort and connection

Study title: Toward Human-Centered Pedagogy in the Age of Generative AI Among Higher Education Educators: Qualitative Phenomenological Study

Evidence type
Interviews or qualitative study
Sample
n = 23
Sources
1 sourcePeer-reviewed
Published
2 min read
Truvace record
TRV-2026-2457cite or check it

Study title: Toward Human-Centered Pedagogy in the Age of Generative AI Among Higher Education Educators: Qualitative Phenomenological Study

A phenomenological interview study of 23 educators from 3 Palestinian universities examined how generative AI is changing everyday teaching. Interviews ran from March to April 2026 and produced 8 interconnected themes. Participants said they valued creativity, personalization, flexibility and instructional support, but worried about dependence, reduced intellectual effort and weaker human connection.

Read the study abstract

Background Generative AI (GenAI) expands possibilities for content generation, feedback, personalization, and instructional support in higher education. Its growing use also challenges established assumptions about teaching, assessment, professional expertise, academic integrity, and educational responsibility. Although research has examined technology adoption and AI competencies, less is known about how educators experience and interpret the pedagogical transformation associated with GenAI in everyday teaching…

45Gain side

How the two sides scoreProblem scores higher

50Problem side

The gain

In interviews with 23 educators at 3 Palestinian universities, participants said generative AI offered creativity, personalization, flexibility and instructional support.

The problem

In interviews with 23 educators at 3 Palestinian universities, participants said generative AI risked student dependence, reduced intellectual effort and weaker human connection.

A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.

What the scores mean

G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.

P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.

In brief

Educators described their work shifting from transmitting content toward mediation, inquiry, reflective learning and process-oriented assessment. Their expertise increasingly centered on facilitation, pedagogical design, ethical guidance and judging AI-generated content, while humans kept responsibility for judgment and educational consequences.

The authors propose a human-centered pedagogy framework and recommend that universities add support for pedagogical design, critical evaluation, process-oriented assessment and ethical reasoning to technical training and policies. The findings reflect educators' accounts, not measured student outcomes.

Main points

  1. The study interviewed 23 educators from 3 Palestinian universities, chosen to vary in academic rank, discipline, teaching experience and AI experience.
  2. Interviews were semistructured and in-depth, held March to April 2026, and analyzed with reflexive thematic analysis informed by phenomenology.
  3. Analysis produced 8 interconnected themes; informational sufficiency appeared after about 20 interviews, with 3 more used to examine the thematic structure.
  4. Educators described a move from content transmission to mediation, inquiry, reflective learning, creative exploration and process-oriented assessment.
  5. Credibility, bias, authorship, authenticity and academic integrity became embedded as pedagogical concerns.
  6. Human-AI collaboration took bounded and integrated forms, with humans retaining responsibility for judgment and educational consequences.
  7. Participants voiced concerns about dependence, reduced intellectual effort and weakened human connection.

The full story

The study addressed a gap: research has covered technology adoption and AI competencies, but less is known about how educators experience and interpret pedagogical change from generative AI in daily teaching. It focused on practices, professional roles, agency, human-AI relationships, opportunities and tensions.

Sampling was purposive with maximum variation. Interviews were audio-recorded, transcribed verbatim and returned to participants for review. Coding was inductive, with human-centered AI and sociomateriality used afterwards as sensitizing concepts.

Participants described professional expertise as facilitation, pedagogical design, contextual interpretation, ethical guidance and evaluation of AI output. Educator agency showed up in decisions about when AI participates, protecting independent student reasoning and setting the limits of human expertise.

The resulting framework puts pedagogical intentionality, contextual judgment, negotiated agency and relational presence at the center of responsible integration. The authors say universities should pair technical training and policies with support for pedagogical design and ethical reasoning.

What this doesn’t fix

The findings come from interviews with 23 educators at 3 Palestinian universities, so they capture educators' own accounts rather than measured effects on learning.

Talk it overFour questions for a class, a team or the dinner table

  1. This record found a gain and a problem in the same place. Which matters more to you, and who might answer differently?

    The gain In interviews with 23 educators at 3 Palestinian universities, participants said generative AI offered creativity, personalization, flexibility and instructional support.

    The problem In interviews with 23 educators at 3 Palestinian universities, participants said generative AI risked student dependence, reduced intellectual effort and weaker human connection.

  2. How do we know? What can this kind of evidence show, and what can’t it?

    The evidence This rests on interviews or another qualitative study with a sample of 23, from JMIR Medical Education.

  3. What does it leave open? What else would you want to know before acting on it?

    The record’s own caveat The findings come from interviews with 23 educators at 3 Palestinian universities, so they capture educators' own accounts rather than measured effects on learning.

  4. Have you seen AI used this way where you study, work or live? Did it go the same way?

The questions are the same on every record. The details under them are taken from this record as written.

Sources

1 on file
  1. JMIR Medical Educationdoi:10.2196/105172
    Peer-reviewed2026-11-06

The debate

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