Artificial Intelligence Applied in Problem-Oriented Learning for Information Retrieval in a Gamified Environment

Antonio Fernandes Soares Netto, Claudio Gottschalg Duque, Tomas Orlandi

doi:10.32038/sem.2026.19.02

European KnowledgeDevelopment Institute

Original Research

Artificial Intelligence Applied in Problem-Oriented Learning for Information Retrieval in a Gamified Environment

Antonio Fernandes Soares Netto

Claudio Gottschalg Duque

Tomas Orlandi

Studies in Educational Management, Volume 19, Pages 17-34, https://doi.org/10.32038/sem.2026.19.02

Abstract
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This study investigates the application of Artificial Intelligence (AI) in a Problem-Based Learning (PBL) environment, with emphasis on information retrieval in the field of public technology procurement, from the perspective of risk management and the use of gamified strategies. The research followed a qualitative/quantitative approach in five stages: development of a case study supported by AI; creation of an original board game to simulate real decision-making situations; identification and categorization of perceived risks; application of a structured Likert-scale questionnaire to measure perception; and critical discussion of the results. This research involved professionals directly engaged in ICT acquisition processes and demonstrated that the use of games adapted to the organizational reality (serious game) enhanced engagement, supported knowledge retention, and broadened awareness of relevant risks. Among the main findings were concerns with supplier lock-in and cost underestimation, as well as the high acceptance rate (93.8%) of the gamified experience compared to the traditional expository model. It is concluded that the integration of AI, gamification, and risk management constitutes an innovative and replicable methodology, capable of strengthening training in complex governmental environments, while fostering effective communication and more informed decision-making.

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Keywords

Information Retrieval; Technology Procurement; Gamification; Risk Management; Problem-Based Learning

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Gamification and Multimodality: A New Path for Education

Author(s) Information

Antonio Fernandes Soares Netto

Brazilian public-sector technology expert with over 20 years of experience in IT, specializing in public procurement, digital governance, and innovation. He has held senior leadership roles in multiple government institutions, including INFRA SA, the Office of the Attorney General (AGU), the Ministry of Management and Innovation (MGI), and the Presidency of the Republic, leading major projects in ICT planning, risk management, and digital transformation. A former World Bank consultant and mentor to more than 5,000 public managers across Brazil, he has also founded companies in the technology, financial, and education sectors. He is the author of three books, holds a software patent (BeeGame), and received national recognition from the Brazilian Military Academy and the Legislative Chamber of the Federal District for his contributions to innovation and public management.

Claudio Gottschalg Duque

Modern Literature degree, with qualifications in Portuguese and German, from the Faculty of Arts of the Federal University of Minas Gerais-UFMG (1994), master's degree in Psycholinguistics from the Postgraduate Program in Linguistic Studies of the Faculty of Arts of the Federal University of Minas Gerais (1998), Sandwich Doctorate. in computational linguistics at Angewandte Sprachwissenschaft und Computerlinguist - Justus-Liebig-Universität Giessen (2003/2004), and Ph.D. in Information Production and Management in the Postgraduate Program in Information Science at the University's School of Information Science UFMG (2005). Associate Professor at the School of Information Science at the University of Brasília (FCI-UnB), since 2006, and effective member of the Postgraduate Program in Information Science (PPGCINF/FCI-UNB), since 2007, and collaborator at the UFMG in the Postgraduate Program in Knowledge Management and Organization (PPGGOC) since 2022. Coordinates the research group "Research Expert Group for Intelligent Information in Multimodal Environment using Natural Language Technologies and Ontologies" (R.E.G.I.I.M.E.N.T.O.).

Tomas Orlandi

PhD in Information Science from UnB - University of Brasília / FCI - Faculty of Information Sciences, acting as a member of the research group R.E.G.I.I.M.E.N.T.O. - Information Architecture, Computational Linguistics and Multimodality, Media and Interactivity. Master in Knowledge Management and Information Technology from the Catholic University of Brasília (2002), Specialist in IT Governance from the University of Brasília - UNB (2007), Specialist in Software Engineering from the Catholic University of Brasília (1990) and degree in Data Processing Technologist from the Catholic University of Brasília (1985). Senior Systems Analyst at Empresa Brasileira de Correios - ECT. Undergraduate professor in the areas of systems analysis. Postgraduate professor in the areas of Project Management, BSC focused on IT and Software Engineering. He gives courses and lectures at various academic and business institutions, public and private.

How to cite this article

Netto, A. F. S., Duque, C. G., & Cotta, T. R. (2026). Artificial intelligence applied in problem-oriented learning for information retrieval in a gamified environment. Studies in Educational Management, 19, 17-34. https://doi.org/10.32038/sem.2026.19.02

Funding

Not applicable.

Author Contributions

Antonio Fernandes Soares Netto: Supervision (Lead), Conceptualization (Lead), Writing – Original Draft (Lead), Methodology (Lead), Writing – Review & Editing (Lead).

Claudio Gottschalg-Duque: Conceptualization (support), Methodology (support), Writing – Review & Editing (support).

Tomás Roberto Cotta Orlandi: Supervision (support), Conceptualization (support), Writing – Original Draft (support), Methodology (support), Writing – Review & Editing (support).

All authors: Reviewed and approved the final manuscript

Conflict of Interest

The authors declare that they have no known competing financial interests or personal

relationships that could have appeared to influence the work reported in this paper.

Generative AI Use Disclosure Statement

No generative AI tools were used in the writing or analysis of this manuscript.

Data Availability Statement

Data will be available on request from the authors.

Acknowledgments

Not applicable.

Open Access

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