This classroom action research investigated the effectiveness of Problem-Based Learning (PBL) in enhancing critical thinking skills within English speaking activities at Global Madani Senior High School. Conducted over two iterative cycles with 21 eleventh-grade students, the study employed a mixed-methods approach within a Classroom Action Research (CAR) framework. Quantitative data from pre- and post-tests, analyzed via a paired sample t-test, revealed a statistically significant improvement in students' critical speaking abilities, with the average score rising from 50.71 to 81.90 (p .001). Qualitative data from observations, recordings, and student journals illustrated a clear developmental trajectory: initial engagement in Cycle 1 exposed deficits in argument structure and logical reasoning, prompting the introduction of targeted scaffolds—including explicit argumentation frameworks and peer feedback protocols—in Cycle 2. The findings demonstrate that PBL’s efficacy is significantly amplified when integrated with responsive, reflective CAR cycles and structured linguistic-cognitive supports. The study concludes that embedding PBL within an adaptive CAR process fosters a synergistic environment where authentic problem-solving motivates communication, and deliberate scaffolding transforms engagement into disciplined, critical spoken discourse, offering a replicable model for enhancing higher-order thinking in EFL contexts.

The purpose of this article is to describe and explore university students´ representations on argumentation and the ways they relate to this practice. From the theoretical framework of academic argumentation and literacy, we analyse the answers to a questionnaire completed by students of a general subject of an argentine public university. The hypothesis that guides our research is that these representations will influence the way students are linked to academic argumentative practices whether this encourages significant learning or not. The corpus was analyzed from a qualitative point of view that was complemented with quantitative tools for data processing. Among the results, we mention the existence of a distance and tension between the internalization and understanding of theoretical concepts linked to argumentation and the development of good argumentative practices, especially when we focus on arguments of a particular area of knowledge.

The study developed an assessment as learning instrument to foster the argumentation skills of pre-service chemistry teachers. The instrument was designed as an alternative assessment tool that integrates self-assessment, peer assessment, and collaborative discussions on an AI-assisted online whiteboard aligned with Toulmin’s argumentation framework. The ADDIE model guided the development of the instrument. The instrument underwent expert content and construct validation, achieving high validity ratings. Pilot testing with 31 participants demonstrated moderate inter-rater reliability (ICC = 0.621) for peer assessments and moderate correlations between self, peer, and instructor assessments, highlighting the need for improved feedback mechanisms and self-assessment training. The analysis of collaborative argumentation discussion revealed that most arguments demonstrated moderate quality, and a smaller proportion achieved the highest quality level. The analysis of practicality and effectiveness gave moderate ratings. Feedback expressed a need for more explicit instructions, more task time, and improved feedback mechanisms. Assessment as learning based instrument was moderately effective but demonstrated some potential for encouraging reflective, collaborative, and logical argumentation processes. Further refinements are needed to optimize its impact.

On the Complexity of the Grounded Semantics for Infinite Argumentation Frameworks

The article analyzes the potential of formal argumentation logic in legal discourse. Using the example of the Ukrainian Law on the State Language, it examines the concept of Abstract Argumentation Framework and defeasible logic. It is demonstrated that formal models of argumentation can not only describe theoretical relations between arguments but also model real mechanisms of legal decision-making.

As large language models (LLMs) are increasingly used to support learning, there is a growing need for a principled framework to guide the design of LLM-based tools and resources that are pedagogically effective and contextually responsive. This study proposes a framework by examining how prompt engineering can enhance the quality of chatbot responses to support middle school students’ scientific reasoning and argumentation. Drawing on learning theories and established frameworks for scientific argumentation, we employed a design-based research approach to iteratively refine system prompts and evaluate LLM-generated responses across diverse student input scenarios. Our analysis highlights how different prompt configurations affect the relevance and explanatory depth of chatbot feedback. We report findings from the iterative refinement process, along with an analysis of the quality of responses generated by each version of the chatbot. The outcomes indicate how different prompt configurations influence the coherence, relevance, and explanatory processes of LLM responses. The study contributes a set of critical design principles for developing theory-aligned prompts that enable LLM-based chatbots to meaningfully support students in constructing and revising scientific arguments. These principles offer broader implications for designing LLM applications across varied educational domains.

Abstract Argumentation Frameworks (AAFs) and Normal Logic Programs (NLPs) are closely related formalisms for which many equivalences have already been elicited. In this paper, we extend this line of research by considering Bipolar Argumentation Frameworks (BAFs), in which arguments have an explicit support relation, independent of the attack relation. We provide direct translations from BAFs to NLPs (and vice versa) in a one-to-one correspondence between several argumentation and 3-valued logic programming semantics. This includes the equivalence involving L-stable semantics. Besides, we deepen the connection between NLPs and BAFs by finding subsets of them for which the proposed translations are each other’s inverse up to isomorphism.

This paper introduces an argumentation framework, differential equation based on cost for recursive argumentation graph (DECRAG), for natural language arguments. DECRAG’s argumentation graph is so flexible that attack and support relations, recursive relations, similarity relations between arguments, and default values can be represented. The base score and the necessity relation can be simulated by the framework. DECRAG maps an argumentation graph to a general system, and the solution of the system is used as the values of the nodes and edges of the graph. We illustrate interesting solutions such as multiple fixed points for the dilemma of even-length cycles and limit cycles for the paradox of odd-length cycles. DECRAG also satisfies many important properties. In this paper, focusing on the theoretical aspect of DECRAG, we show examples, properties, and evaluations of the framework.

The verification problem in abstract argumentation consists of checking whether a set is acceptable under a given semantics in a given argumentation graph. Explaining why the answer is so is the challenge tackled by this paper. Visual explanations in the form of subgraphs of the initial argumentation framework are defined. These explanations are grouped into classes, allowing one to select the explanation that suits them best among the several offered possibilities. Results are provided on how to use the visual aspects of these explanations to support the acceptability of a set of arguments under a semantics. Computational aspects of specific explanations are also investigated.

Multi-Agent Systems represent a fundamental paradigm in distributed artificial intelligence where autonomous computational entities collaborate within shared environments to accomplish individual and collective objectives. The inherent complexity of these systems creates scenarios where agents encounter conflicting goals or maintain contradictory information about their operational environment. This comprehensive technical review examines contemporary conflict resolution techniques employed in multi-agent systems, analyzing their theoretical foundations, practical implementations, and effectiveness across various application domains. The article categorizes conflict resolution techniques into three primary areas: goal conflict resolution through negotiation-based approaches and auction-based mechanisms, information conflict resolution via belief merging and consensus algorithms alongside voting systems, and structured decision-making approaches utilizing priority rules and argumentation frameworks. Each technique demonstrates distinct advantages in specific operational contexts, with negotiation-based systems excelling in resource allocation scenarios, auction-based mechanisms proving effective in competitive environments, and belief merging techniques providing robust solutions for information inconsistencies. The article explores potential applications across diverse sectors, including robotics swarms, financial systems, and smart grid infrastructure, indicating promising directions for conflict resolution mechanism implementation. The review concludes by identifying emerging research opportunities, including the integration of artificial intelligence and machine learning techniques to enhance system adaptability and address scalability challenges in large-scale deployments.

Abstract The odious debt doctrine holds that sovereign debts incurred for illegitimate purposes, such as human rights abuses or the personal enrichment of rulers, should not bind successor governments. Although its legal status remains contested, the doctrine has been invoked in various cases where prior regimes misused borrowed funds. This article considers whether similar principles can be applied to foreign investment, particularly when investors support oppressive regimes or engage in harmful activities. It examines how arbitration tribunals have addressed investor misconduct and argues that the reasoning in several arbitral awards points to an emerging doctrine of “odious investment,” justifying the repudiation or limitation of investment treaty obligations with odious investors. Recognizing such a doctrine would provide a legal basis for states to challenge investor claims while also offering a new interpretive and discursive framework for shaping legal arguments and policy. By bridging the fields of sovereign debt and international investment law, the article calls for a systematic approach to investor accountability and the legal consequences of odious financing.

Reasoning under incomplete information is an important research direction in the study of computational argumentation. Most advances in this direction so far have focused on abstract argumentation frameworks. In particular, development of computational approaches to reasoning under incomplete information in structured formalisms remains to a large extent a challenge. We address this challenge by studying the problems of determining stability and relevance—with the aim of analyzing aspects of resilience of acceptance statuses in light of new information—in the central structured formalism of ASPIC+ . The specific ASPIC+ instantiation and grounded argumentation semantics we focus on are motivated by current applications in criminal investigation at the Netherlands Police. Our contributions consist of a theoretical analysis of the complexity of deciding stability and relevance as well as first exact algorithms for reasoning about stability and relevance in incomplete ASPIC+ theories. In terms of complexity results, we show that deciding stability is coNP-complete for incomplete ASPIC+ when assuming a preference ordering on defeasible rules via the last-link ordering, while deciding relevance is significantly more complex, namely NP^NP-complete. Complementing the complexity results, we develop practical algorithms for deciding stability and relevance based on the declarative paradigm of answer set programming (ASP). Furthermore, we provide an open-source implementation of the algorithms, and show empirically that the implementation exhibits promising scalability on both real-world and synthetic data. Our exact approach to stability is competitive with a previously proposed inexact approach, and the run times of our algorithms for both stability and relevance are sufficiently low on real-world data to be used in online settings.

Abstract This article discusses the novel Yeni Turan written by Halide Edip Adıvar during the Second Constitutional Era, within the framework of the liberal nationalism argument. Liberal nationalism is a conception of nationalism in which liberal values are dominant. Liberal nationalists work from the idea that the world is divided into nations, each with a unique personality and character that deserve respect. The liberal nationalist argument considers the preservation and development of national identities as important. According to liberal nationalists, this means that national identities should have the right to self-determination. Additionally, liberal nationalists see an environment that nurtures national identities as compatible with democratic practices if individuals share the same beliefs as those representing them, this indicates to the members of that nation that they are part of an active community. The clearest way to ensure this is for nations to have a government of their own choosing. This perspective sheds light on the relationship between nationalism and democracy. Thus, a political structure where sovereignty is shared under a federal umbrella would be a governance model aligned with the ideology of liberal nationalism. As a nationalist intellectual of the Second Constitutional Era, Halide Edip Adıvar, in her utopian novel Yeni Turan, written during the Balkan Wars, proposes granting “decentralization” to the elements within the empire in response to the state’s decline. In this novel, where nationalist elements are prominently featured, we witness Halide Edip Adıvar’s “liberal nationalist” ideas.

The present research proposes a conceptual model that explains how digital marketing communication strategies play a role in shaping nation brand equity amid geopolitical crises. This assertion is supported by a case study of the Russia-Ukraine war. The research adopts a model-based conceptual approach, integrating literature on digital narrative content, social media diplomacy and digital multichannel orchestration as key variables that influence perceived trust, emotional engagement and digital brand reputation. Utilizing Toulmin's argument framework, the research offers a logical justification for the relationship between the variables, thereby demonstrating that authentic and emotional digital narratives foster global public trust, while digital diplomacy enhances country engagement and credibility, and multichannel orchestration strengthens reputation through cross-platform message consistency. This study concludes that digital marketing communication is not merely a promotional tool, but a strategic instrument in building global perceptions during times of crisis. This research makes a theoretical contribution by expanding the scope of brand equity theory from the business context to the geopolitical realm. Furthermore, it provides a pragmatic framework for states to formulate integrated and responsive digital communication strategies in periods of conflict.

Clinical decision-making involves complex considerations, such as treatment options, patient preferences, and conflicting medical opinions. Argumentation frameworks provide a structured approach to modeling and reasoning through these complexities by constructing and evaluating arguments for and against specific decisions. This paper reviews existing AFs to identify the most suitable one for clinical decision support. We perform a comparative analysis of several frameworks-Dung's AF, PAF, VAF, WAF, BAF, IBIS, and QuAD-using criteria tailored to clinical decision-making needs. Our analysis suggests that QuAD and DF-QuAD are those that best satisfy our criteria.

Constructing arguments, applying logical reasoning, and developing intellectual skills are fundamental to academic success in postgraduate education and qualitative research. The study objective of this paper aims at critically analyzing argument construction, logical reasoning, and intellectual skill development as fundamental components of postgraduate education and qualitative research. The analysis highlights the importance of these elements in fostering critical engagement, advancing knowledge, and contributing to scholarly discourse. The paper draws on academic literature to offer a nuanced interpretation of these interconnected dimensions and explores strategies to enhance argumentation, reasoning, and intellectual skills in postgraduate education. Argument construction is identified as the cornerstone of academic dialogue, requiring structured claims supported by evidence and reasoning. The analysis supports this assertion using evidence from recent studies highlighting the value of argumentation training in enhancing critical thinking and research writing, advocating for its integration into postgraduate curricula. Notably, findings from previous studies link organized argumentation to students’ analytical skills and ability to interact with complex data. The analysis also reports that logical reasoning is the cornerstone of effective argumentation, offering systematic methods to connect premises to conclusions. Deductive reasoning is highlighted for its role in hypothesis testing and causal analysis, ensuring precise and reliable conclusions. Inductive reasoning, a bottom-up approach, uncovers patterns and trends from specific observations, proving essential for theory development and exploratory research. Abductive reasoning facilitates plausible explanations for poorly defined phenomena, while retroductive reasoning identifies underlying causes to generate alternative theoretical models. Results of the assessment also emphasize the need for postgraduate students to develop intellectual skills, including critical thinking, synthesis, and dialectic reasoning. Notably, student scholars employ this skill to engage deeply with subject matter and navigate complex phenomena. Structured educational interventions, such as formal logic training and argumentative writing activities, significantly enhance higher-order thinking skills. Technology, with its transformative role, is reshaping the landscape of education. AI tools and digital learning platforms offer real-time feedback, interactive tasks, and reflective thinking opportunities, paving the way for a more engaging and practical learning experience. An assessment of extant literature also reveals that experiential learning and problem-based frameworks strengthen intellectual skills by connecting theoretical concepts with real-world applications. Interdisciplinary research, collaborative projects, and dynamic problem-solving contexts prepare students for professional challenges and foster adaptability. These approaches underscore the importance of integrating academic learning with hands-on experiences to enhance intellectual engagement and flexibility. The analysis confirms that constructing arguments, applying logical reasoning, and developing intellectual skills are essential for postgraduate education and qualitative research. Deductive, inductive, abductive, and retroductive reasoning methods provide a viable framework for argumentation, while intellectual skills enable researchers to navigate complex phenomena and contribute effectively to their disciplines. The value of interdisciplinary collaboration is underscored, as it enriches research with diverse perspectives and fosters a more comprehensive understanding of complex phenomena. Future research should explore innovative strategies to support argument construction, interdisciplinary collaboration, and intellectual growth, ensuring the continued evolution of postgraduate education and academic inquiry.

Abstract The goal of argument mining, or AM, is to identify the argumentative structures in a document. Prior approaches necessitate a number of subtasks, including component classification, relation classification, and span identification. Therefore, rule-based postprocessing is required for these methods to extract generative structures from each subtask's output. This method increases the model's complexity and broadens the hyperparameter search space. We suggest a straightforward yet effective technique based on a text-to-text generation strategy employing a pretrained encoder-decoder language model to overcome this challenge. Our approach eliminates the requirement for task-specific postprocessing and hyperparameter optimisation by producing argumentatively annotated text for spans, components, and relations all at once. Additionally, as it is a simple text-to-text creation method, we may readily modify our strategy to fit different kinds of argumentative frameworks. Experimental findings show that our strategy works well, achieving state-of-the-art performance on three distinct benchmark datasets: the Cornell eRulemaking Cor- pus (CDCP), AbstRCT and the Argument-annotated Essays Corpus (AAEC). Keywords: Argument Mining, Text-to-Text Generation, T5

Abstract This study explores the evolving cooperation perspectives of Central Asian states against the backdrop of an ongoing war between Russia and Ukraine. This protracted conflict has triggered regional and global geopolitical shifts, prompting reassessment of the roles and strategies of the various global and regional actors. Focusing on Kazakhstan, Kyrgyzstan, Uzbekistan, Turkmenistan and Tajikistan, this research employs the argumentative framework of Political Discourse Analysis to examine the prospects for cooperative actions drawing insights from five Central Asian leaders’ 2022 speech in Cholpan-Ata. Guided by the shelter-seeking foreign policy strategy, the findings reveal Central Asian leaders’ commitment to pursuing a cohesive regional strategy as an immediate necessity to address shared challenges, including security, economic and infrastructural development and water resource management to promote enhanced cooperation within the region. The study advocates for taking proactive, tangible, and assertive policy measures to realize the proposed actions, emphasizing the importance of a unified approach in navigating the complex geopolitical dynamics.

Abstract This article zeroes in on the distinctive features of a novel site of public argumentation. The UK drill music decision of the Oversight Board (OSB) of Meta will be analyzed given its salience as its most explicit decision regarding governmental requests. An introduction presents a theoretical framework. The second section describes the interdiscursively hybrid genre of the OSB. The third section recalls the case, highlighting its complex relation to several discourses and legalities. The fourth section analyzes the argumentation undertaken in the OSB’s decision, looking at its generic moves and structure, and its hallmarks: the separation of balancing and proportionality assessments, the incorporation of non-merits-based arguments, and the development of an interface doctrine (as a recurring argumentative framework in which norms and arguments from other institutional sites are assessed). A conclusion remarks the relevance for the public realm of analyzing hybrid genres and interlegal sites of argument.

The connection between inconsistent databases and Dung’s abstract argumentation framework has recently drawn growing interest. Specifically, an inconsistent database, involving certain types of integrity constraints such as functional and inclusion dependencies, can be viewed as an argumentation framework in Dung’s setting. Nevertheless, no prior work has explored the exact expressive power of Dung’s theory of argumentation when compared to inconsistent databases and integrity constraints. In this paper, we close this gap by arguing that an argumentation framework can also be viewed as an inconsistent database. We first establish a connection between subset-repairs for databases and extensions for AFs considering conflict-free, naive, admissible, and preferred semantics. Further, we define a new family of attribute-based repairs based on the principle of maximal content preservation. The effectiveness of these repairs is then highlighted by connecting them to stable, semi-stable, and stage semantics. Our main contributions include translating an argumentation framework into a database together with integrity constraints. Moreover, this translation can be achieved in polynomial time, which is essential in transferring complexity results between the two formalisms.