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Enhancing Pedagogy with Artificial Intelligence
Get PDFThis guidebook provides an introduction to Artificial Intelligence (AI) and pedagogy for beginners. It explains fundamental AI concepts and essential tools for teachers, addressing key challenges at the intersection of teaching and technology. The guide outlines various methods for incorporating AI into curriculum design and classroom activities to develop critical thinking skills and modernize teaching practices
Automatizuotas sprendimų priėmimas teisme: dirbtinio intelekto naudojimas rengiant ir priimant teismo sprendimus
Get PDFThe integration of Artificial Intelligence (AI) into the judicial system presents both opportunities and challenges. AI can expedite proceedings, reduce costs, and broaden access to justice by serving as a decision-making assistant or an autonomous decision-maker. The article is structured into three main parts: an overview of AI technologies and their classification, a detailed examination of AI‘s role as an assistant in judicial decision-making, and a consideration of AI as an autonomous decision-maker.The analysis revealed that while AI can significantly assist in legal proceedings by offering preliminary judgments or legal advice, its capacity as an autonomous decision-maker is complex.A robust legal foundation respecting procedural norms and Article 6 of the European Convention on Human Rights (ECHR) is crucial. This legal framework should define AI‘s operational boundaries within the judiciary to prevent infringement on the right to a fair trial. Moreover, in line with Article 22 of the General Data Protection Regulation (GDPR), there must be opportunities for human intervention and the ability to contest AI-generated decisions, safeguarding a human-centric approach to justice.The issue of bias in AI, reflecting pre-existing prejudices in training data, underscores the importance of careful programming, dataset selection, and ongoing oversight to avoid perpetuating discriminatory practices. AI‘s potential in simulating legal reasoning in straightforward cases suggests a cautious yet optimistic engagement with technology, advocating for its selective application in scenarios where public hearings are unnecessary.The paper concludes that while AI presents a promising tool for enhancing judicial processes, its use must be approached with caution. It advocates for a balanced, multi-faceted approach to AI integration, emphasizing ongoing evaluation, legal regulation, and the selective application of AI technologies.Dirbtinio intelekto (DI) integravimas į teismų sistemą teikia ir galimybių, ir iššūkių. Dirbtinis intelektas žada pagreitinti teismo procesą, sumažinti išlaidas ir išplėsti galimybes kreiptis į teismą, nes jis gali būti sprendimų priėmimo asistentas arba savarankiškas sprendimų priėmėjas. Straipsnį sudaro trys pagrindinės dalys: dirbtinio intelekto technologijų apžvalga ir jų klasifikacija, išsamus dirbtinio intelekto kaip pagalbininko vaidmens priimant teisminius sprendimus nagrinėjimas ir dirbtinio intelekto kaip savarankiško sprendimų priėmėjo aptarimas.Analizė atskleidžia, kad nors DI gali reikšmingai padėti teisminiuose procesuose siūlydamas preliminarius sprendimus ar teisines konsultacijas, jo kaip savarankiško sprendimų priėmėjo galimybės yra sudėtingos. Labai svarbu sukurti tvirtą teisinį pagrindą, kad būtų laikomasi procesinių normų ir Europos žmogaus teisių konvencijos (EŽTK) 6 straipsnio. Šis teisinis pagrindas turėtų apibrėžti DI veiklos ribas teismų sistemoje, kad būtų išvengta teisės į teisingą bylos nagrinėjimą pažeidimų. Be to, pagal Bendrojo duomenų apsaugos reglamento (BDAR) 22 straipsnį turi būti numatytos žmogaus įsikišimo galimybės ir galimybė užginčyti dirbtinio intelekto sukurtus sprendimus, užtikrinant į žmogų orientuotą požiūrį į teisingumą.Dėl dirbtinio intelekto šališkumo, kuris atspindi išankstinį nusistatymą mokymo duomenyse, svarbu kruopščiai programuoti, atrinkti duomenų rinkinius ir nuolat prižiūrėti, kad būtų išvengta įtvirtinti diskriminacinę praktiką. Dėl dirbtinio intelekto potencialo imituojant teisinius samprotavimus nesudėtingose bylose siūloma atsargiai, tačiau optimistiškai vertinti technologiją, pasisakant už selektyvų jos taikymą scenarijuose, kai vieši svarstymai nereikalingi.Straipsnyje daroma išvada, kad nors dirbtinis intelektas yra daug žadanti priemonė teisminiams procesams tobulinti, jį naudoti reikia atsargiai. Jame pasisakoma už suderintą, įvairiapusį požiūrį į dirbtinio intelekto integravimą, pabrėžiant nuolatinį vertinimą, teisinį reguliavimą ir selektyvų dirbtinio intelekto technologijų taikymą
Teaching AI Through Student Research Projects: A Short Guide
No full textThis guide is based on practical experience designing and supervising research-based projects on Artificial Intelligence (AI) in the context of social sciences and humanities education. Teaching about AI in these disciplines often presents specific challenges: the fast-changing nature of the field, the difficulty of connecting abstract concepts to real-world implications, and the need to make technical developments accessible without oversimplifying them. Moreover, traditional forms of assessment, such as essays, are being reshaped by the growing use of generative AI.
In this context, short research-based projects offer an engaging and flexible alternative. They encourage students to take ownership of their learning, apply theoretical knowledge to practical questions, and develop essential skills in ethical reasoning, critical analysis, and communication. These projects do not require full- scale research design or long-term commitment but are instead structured to be manageable within a single semester. They are particularly well-suited for interdisciplinary and exploratory learning environments, where the aim is to build understanding, curiosity, and responsible approaches to AI in society
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Ethical Principles of Artificial Intelligence
No full textThis interactive resource introduces learners to the ethical challenges posed by Artificial Intelligence (AI) through scenario-based exercises. It is designed to support AI literacy and foster critical ethical reflection, particularly for those without technical backgrounds. The exercises present realistic case studies in areas such as hiring, medical diagnostics, border control, education, legal services, and content moderation. Each scenario places learners in the role of a decision-maker and requires them to decide on how, when, and whether to use the AI in their professional life. The scenarios also encourage reflection and discussion on broader systemic constraints that may influence individual decision-making.These exercises are accessible across devices and compatible with online learning environments. This resource can be used for classroom teaching, professional training, or self-paced study. It aims to help learners recognize the ethical dimensions of AI, explore the limits of automation, and strengthen their capacity for responsible, human-centered decision-making
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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