Skip to content
#generative ai Open access

Student-Adopted Self-Regulating Strategies of Seeking Help or Information in Higher Education in Lithuania

Sep 2026 · Journal of Education Culture and Society · Vol 17, pp. 535-549
Innovative Teaching and Learning Methods

Abstract

Aim. The present study aimed at establishing self-regulating strategies used by Lithuanian undergraduate students when they could not accomplish their academic assignments due to a lack of necessary information. By establishing data-based evidence regarding student choices of seeking help or information made under the circumstances preventing them from fulfilling academic tasks, the study addresses the existing research gap in the field. Methods. The present research is part of larger mixed-method research (n = 309) conducted at four universities in Lithuania. The data for this study were collected from two university students’ (n = 140) responses to the open-ended question “What strategies do you use if / when you do not have all the necessary information for accomplishing your academic assignments?”. To analyse the data, qualitative methodology was chosen, and the inductive (bottom-up) content analysis was used. Results. Content analysis revealed four major categories (covering five subcategories) of student-adopted self-regulating strategies, such as using a combination of different strategies of seeking help or information, relying exclusively on the strategy of Internet search, using the strategy of seeking help from different stakeholders, and taking no action. Conclusions. It can be concluded that most students sought help proactively, i.e., they used up to three self-regulating strategies to solve the problem of lack of information necessary for their studies, on the one hand. On the other hand, it was concluded that the students who used only one strategy or did not take any action should be encouraged to seek different forms of help or sources of information, which could foster their self-regulated learning. The findings also call for research into the use of Generative AI in undergraduate studies. Cognitive value. This study contributes to research by providing evidence from a Lithuanian case on student-adopted strategies of seeking help or information relevant for the development of students’ self-regulated learning.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

GPT-Lab Oct 12, 2026

AI Has Grown Up. Have We?

AI is moving beyond impressive demos. After World Summit AI 2026 in Amsterdam, GPT-Lab's Muhammad Waseem reflects on what organizations need to make AI agents reliable, responsible and genuinely useful in everyday work. The post AI Has Grown Up. Have We? appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.