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Beyond the Algorithm: Integrating AI into Applied Learning Pedagogy—A Critical Inquiry

This paper examines how artificial intelligence (AI) can strengthen applied learning in the social sciences by transforming raw information into analyzable material that students actively interpret, evaluate, and convert into disciplinary knowledge. It argues that AI, when guided by theoretical rigor and ethical awareness, enhances critical pedagogy by supporting qualitative interpretation, computational analysis, simulation based inquiry, and independent reasoning. Rather than replacing instruction, AI function…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-2176· cite & verify
Beyond the Algorithm: Integrating AI into Applied Learning Pedagogy—A Critical Inquiry
Workshop with students of Yerevan Brusov State University of Languages and Social Sciences (5) by Beko. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

In brief

A paper in the Journal of Applied Learning in Higher Education examines how artificial intelligence might be built into applied learning in the social sciences. It frames AI as a way to turn raw information into material that students analyze, interpret, and evaluate themselves.

The significance lies in its positioning of AI as a support for instruction rather than a substitute for it, which speaks to ongoing debates over AI in higher education. As presented in the abstract, though, the claim is an argument, and no measured learning outcomes are reported, so the size of any benefit is not established.

Main points

  1. The paper focuses on applied learning in the social sciences.
  2. It describes AI as turning raw information into analyzable material that students interpret and evaluate.
  3. It lists qualitative interpretation, computational analysis, simulation-based inquiry, and independent reasoning as supported activities.
  4. It says AI should not replace instruction and should be used with theoretical rigor and ethical awareness.

The gain

The paper argues that AI, guided by theoretical rigor and ethical awareness, can support student qualitative interpretation, computational analysis, simulation-based inquiry, and independent reasoning in social science courses.

The rundown

The abstract presents a critical inquiry rather than an empirical trial. Its central claim is that students gain from working with AI outputs they must question, correct, and place in context, instead of accepting them passively.

The learning goal described is deeper engagement with social structures, patterned behavior, and institutional dynamics. Students are expected to convert analyzed information into disciplinary knowledge.

The paper was published in a peer-reviewed higher education journal with a 2026 date, and the supplied text is limited to the abstract.

Sources

  1. Peer-reviewedJournal of Applied Learning in Higher Education2026-10-21

The debate