Design and Evaluation of an AI-based Learning System to Foster Students\u27 Structural and Persuasive Writing in Law Courses

Structured and persuasive writing is essential for effective communication, convincing readers of argument validity, and inspiring action. However, studies indicate a decline in students\u27 proficiency in this area. This decline poses challenges in disciplines like law, where success relies on structured and persuasive writing skills. To address these issues, we present the results of our design science research project to develop an AI-based learning system that helps students learn legal writing. Our results from two different experiments with 104 students demonstrate the usefulness of our fully working AI-based learning system to support law students independent of a human instructor, time, and location. In addition to providing our embedded software artifact, we document our evaluated design knowledge as a design theory. Thus, we provide the first step toward a nascent design theory for the development of AI-based learning systems for legal writing

Online dispute resolution: an artificial intelligence perspective

Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.The work described in this paper is included in TIARAC - Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), which is a research project supported by FCT (Science & Technology Foundation), Portugal. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009).Acknowledgments. The work described in this paper is included in TIARAC - Telematics and Artificial Intelligence in Alternative Conflict Resolution Project (PTDC/JUR/71354/2006), which is a research project supported by FCT (Science & Technology Foundation), Portugal. The work of Davide Carneiro is also supported by a doctoral grant by FCT (SFRH/BD/64890/2009)

The Diagnosticity of Argument Diagrams

Can argument diagrams be used to diagnose and predict argument performance? Argumentation is a complex domain with robust and often contradictory theories about the structure and scope of valid arguments. Argumentation is central to advanced problem solving in many domains and is a core feature of day-to-day discourse. Argumentation is quite literally, all around us, and yet is rarely taught explicitly. Novices often have difficulty parsing and constructing arguments particularly in written and verbal form. Such formats obscure key argumentative moves and often mask the strengths and weaknesses of the argument structure with complicated phrasing or simple sophistry. Argument diagrams have a long history in the philosophy of argument and have been seen increased application as instructional tools. Argument diagrams reify important argument structures, avoid the serial limitations of text, and are amenable to automatic processing. This thesis addresses the question posed above. In it I show that diagrammatic models of argument can be used to predict students' essay grades and that automatically-induced models can be competitive with human grades. In the course of this analysis I survey analytical tools such as Augmented Graph Grammars that can be applied to formalize argument analysis, and detail a novel Augmented Graph Grammar formalism and implementation used in the study. I also introduce novel machine learning algorithms for regression and tolerance reduction. This work makes contributions to research on Education, Intelligent Tutoring Systems, Machine Learning, Educational Datamining, Graph Analysis, and online grading

Supporting Human Cognitive Writing Processes: Towards a Taxonomy of Writing Support Systems

In the field of natural language processing (NLP), advances in transformer architectures and large-scale language models have led to a plethora of designs and research on a new class of information systems (IS) called writing support systems, which help users plan, write, and revise their texts. Despite the growing interest in writing support systems in research, there needs to be more common knowledge about the different design elements of writing support systems. Our goal is, therefore, to develop a taxonomy to classify writing support systems into three main categories (technology, task/structure, and user). We evaluated and refined our taxonomy with seven interviewees with domain expertise, identified three clusters in the reviewed literature, and derived five archetypes of writing support system applications based on our categorization. Finally, we formulate a new research agenda to guide researchers in the development and evaluation of writing support systems

Teaching Law by Design: How Learning Theory and Instructional Design Can Inform and Reform Law Teaching

This Article examines the law school Vicarious Learning/Self Teaching Model in light of learning theory and instructional design. Further, it identifies both the good intuitions\u27 and the many deficiencies in how law professors develop and present instruction. More importantly, this Article offers a dramatically different approach to law school instruction, an approach more likely than current law teaching methodologies to produce effective, efficient, and appealing law school instruction