Sirvi Märksõna "artificial intelligence (AI)" järgi

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Documenting AI use in humanities research
(Tartu University Library, 2025) Huvila, Isto; Nermo, Magnus; Papadopoulou Skarp, Frantzeska; Tienken, Susanne; Widholm, Andreas; Blåder, Anna; Verhagen, Harko; Fridlund, Mats
This paper explores the critical need to document the use of Artificial Intelligence (AI) in humanities research. While AI offers efficiency and analytical power, its application raises concerns about transparency, bias, and reproducibility. Existing documentation frameworks often emphasize technical aspects, overlooking the human and contextual dimensions vital to humanities scholarship. Drawing on cross-disciplinary literature, the paper advocates for integrating paradata (process-related meta-information) to capture both technical and human facets of AI use. It proposes shifting the focus from speculative future needs to documenting the transformation AI is intended to achieve within specific research contexts. Practical strategies include combining automated tools with reflective documentation practices and providing clear explanations of the purpose and expected outcomes of AI use. The paper calls for infrastructural support and a rethinking of documentation sufficiency to enhance understanding, reuse, and accountability in humanities research.
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Solving the “Databases” SQL Assignments with Language Models in Comparison with Students’ Results
(Tartu Ülikool, 2025) Lember, Joosep; Luik, Piret, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
In recent years, the use of AI-based language models such as ChatGPT and Microsoft Copilot has become common in education. However, it remains unclear how well these tools can independently solve more complex tasks. The aim of this bachelor’s thesis was to evaluate the ability of these language models to solve homework assignments from the University of Tartu’s “Databases” course and compare the results with those of students from the 2025 spring semester. The assignments were completed using the language models in two ways: first without any prior information about the database structure, and then with the relevant structural information provided. The results were evaluated using automatic grading, and the types and accuracy of errors were analyzed. The models performed better when they had access to the database structure, but overall still fell short of student performance, especially in complex and ambiguous tasks.