AI and the Changing Landscape of
Higher Education
Across the globe, universities are
adopting artificial intelligence to personalize learning, provide instant
feedback, and support students outside traditional classroom hours. In
Indonesia, this shift aligns with broader digital transformation policies in
higher education, where AI-powered platforms, chatbots, and recommendation
systems are increasingly common.
Yet questions remain about how AI
affects the quality of student thinking. Does AI encourage deep
reasoning, or does it promote surface-level learning? Understanding this
distinction is crucial for educators and policymakers who want AI to
strengthen, not weaken, critical thinking skills.
This study places those questions
within the Community of Inquiry (CoI) framework, a widely used
model for analyzing learning in digital environments. The framework describes
four phases of cognitive presence: triggering, exploration,
integration, and resolution. Together, these
phases reflect how learners move from curiosity to understanding and finally to
application.
How the Research Was Carried Out
The research was led by Mayasari
of Universitas Raharja, with co-authors Agustinus Prasetyo Edy
Wibowo from Politeknik Perkeretaapian Indonesia Madiun and Syamsudin
from Universitas Islam Negeri Maulana Malik Ibrahim. The team
conducted a qualitative case study involving 12 university students
who regularly used AI-based learning platforms at a technology-focused
institution in Gading Serpong, Indonesia.
Data came from semi-structured
interviews, observations of digital learning activities, and analysis of
student–AI interactions within learning management systems. Rather than
measuring test scores, the researchers focused on how students described their
thinking processes and how those processes evolved when AI was involved.
Key Findings: AI Helps Start
Thinking, Not Finish It
The study identified clear patterns in how AI supports different stages of cognitive presence.
- Triggering phase: AI sparks curiosity AI was most effective at initiating learning. Adaptive feedback, error notifications, and guiding questions helped students quickly recognize gaps in their understanding. Many students described AI prompts as “alerts” that pushed them to think more critically about what they did not yet understand.
- Exploration phase: AI expands information searching During exploration, students used AI to request explanations, examples, and alternative perspectives. AI acted as a conceptual guide, helping learners compare ideas and test assumptions. Students with higher digital literacy benefited the most, as they were better at asking focused questions and navigating AI responses.
- Integration phase: understanding still requires reflection Despite strong support in early stages, AI played a limited role in helping students synthesize knowledge. Integration—connecting ideas into a coherent understanding—mostly happened through students’ own reflection, note-taking, and comparison of sources. Students who relied too heavily on AI responses tended to show shallower integration.
- Resolution phase: application is rare and selective The final stage, where students apply knowledge to solve problems or make decisions, appeared only among students with strong self-regulation and digital literacy. AI functioned more as a checker or evaluator at this stage, not as a driver of application. Many students stopped once they felt they “understood” a concept, without moving on to practical use.
Why Digital Literacy and
Self-Regulation Matter
One of the most important conclusions
is that AI does not automatically create deep learning.
Students who actively managed their learning, reflected on AI feedback, and
questioned responses were far more likely to reach higher levels of cognitive
presence. In contrast, students who treated AI as a shortcut often remained at
the exploration stage.
As the authors note, AI can
function as a cognitive catalyst, but it cannot replace the learner’s role in
reflection and decision-making. This insight highlights the importance of
teaching students how to use AI critically, not just how to access it.
Implications for Universities and
Policymakers
The findings offer practical guidance for higher education institutions.
- For educators: AI should be paired with reflective assignments, project-based learning, and problem-solving tasks that push students beyond exploration toward integration and application.
- For universities: Digital literacy and self-regulation skills need to be embedded in curricula so students can use AI as a thinking partner rather than an answer machine.
- For policymakers: AI adoption strategies should focus on pedagogy, not just technology, ensuring that learning outcomes align with national goals for critical and independent thinking.
As AI becomes more common in
Indonesian higher education, these insights can help institutions design
learning environments that genuinely support intellectual growth.
Author Profile
Mayasari is a
lecturer and researcher at Universitas Raharja, specializing
in educational technology and AI-assisted learning. Agustinus Prasetyo
Edy Wibowo is affiliated with Politeknik Perkeretaapian
Indonesia Madiun and focuses on applied technology and vocational
education. Syamsudin is a lecturer at Universitas
Islam Negeri Maulana Malik Ibrahim, with expertise in educational
research and digital learning.
Source
Journal Article: Exploring
Students’ Cognitive Presence in AI-Assisted Learning Environments: A
Qualitative Inquiry in Higher Education
Journal: Asian
Journal of Applied Education
Year: 2026
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