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.
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· EUROMICRO Conference on Soft...· 64 citations· ⚡6
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.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
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.· International Conference on...· 48 citations· ⚡4
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.· arXiv.org· 44 citations· ⚡2
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.· arXiv.org· 41 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
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.
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.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026