CoChat
Sign Up
Cited by 1Educational Strategies and EpistemologiesLibrary Science and Information LiteracyInformation Retrieval and Search Behavior

The DIPS model: GenAI integration in digital information problem solving by experts and novices

Open access
Boetje, Josien, de Graaf, Zindzi, Wopereis, Iwan, van Ginkel, Stan O., Smakman, Matthijs H.J., Versendaal, Johan, Barendsen, Erik·2026·Computers & Education
DOI 10.1016/j.compedu.2026.105677
Open this paper in CoChat — free account

Abstract

In the digital age, academic success increasingly depends on information problem solving (IPS) competence: the ability to find, evaluate, process, and synthesize information into knowledge products. However, generative AI (genAI) transforms how higher education students engage with digital information, challenging models developed for web-based IPS, such as the IPS using Internet (IPS-I) model. Therefore, an empirically grounded model for AI-assisted IPS process is needed. This study decomposes the cognitive activities of the AI-assisted IPS process and pinpoints where students need guidance and support. It empirically refines the IPS-I model through cognitive task analysis of think-aloud protocols from 14 novices (pre-service teachers) and 12 experts (teacher trainers) completing an authentic IPS task using genAI. Through bottom-up analysis of observed behaviors, we identified 25 cognitive activities distributed across six interrelated phases: Define, Search, Select, Process, Synthesize, and Create. Compared to the original IPS-I model, three new and four modified activities emerged, with Regulation remaining a central, cross-cutting metacognitive component. Additionally, experts and novices differed in time allocation across phases, AI tool usage, and sequencing of the IPS phases. Experts exhibited a more iterative process, integrating AI across all phases, including Regulation, and delayed the use of AI. In contrast, novices used AI early, mainly during Search and Create. The resulting Digital Information Problem Solving (DIPS) model provides a refined, empirically grounded framework for understanding and teaching IPS within the context of genAI. This model defines the intended learning outcomes for learning environments aimed at fostering responsible AI use in higher education.

Integrity signals

Registry

Crossref · 10.1016/j.compedu.2026.105677

Cites

58 of 58 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Boetje, J., de Graaf, Z., Wopereis, I., van Ginkel, S. O., Smakman, M. H. J., Versendaal, J., & Barendsen, E.
Affiliations
  • University of Applied Sciences Utrecht (NL)
  • Open University of the Netherlands (NL)
  • Radboud University Nijmegen (NL)
Corresponding author
Boetje, Josien
ORCID
Venue
Computers & Education
Publisher
Elsevier BV
Published
2026-11
Volume
253
Pages
105677
ISSN
0360-1315 (ISSN-L)
DOI
10.1016/j.compedu.2026.105677
OpenAlex
W7163005630
Cited by
1
References
58
Citations by year
2026: 1
FWCI
7.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers & Education (publishedVersion, cc-by)
  • Radboud Repository (Radboud University) (submittedVersion)
  • DSpace (Open University in the Netherlands) (submittedVersion, cc-by)
Topics
  • Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
  • Social Sciences › Library and Information Sciences › Library Science and Information Literacy
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Keywords
information problem solving, generative AI, cognitive task analysis, think-aloud, expert-novice differences, pre-service teachers, teacher educators, metacognition, responsible AI use, higher education, digital information literacy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review