Thursday, April 5, 2018
Using decision tree technology in clinical legal education as an alternative to traditional pedagogy to reduce professional errors
This is a new article by Professor Timothy Tarvin (Arkansas) called Combating Professional Error in Bankruptcy Analysis Through the Design and Use of Decision Trees in Clinical Pedagogy and can be found at 91 St. John's L. Rev. 427 (2017). Here's an excerpt from the intro:
The evolution of legal education has been punctuated by dynamic change, but its focus has always been teaching analytical reasoning. “Thinking like a lawyer” is the gold standard and goal of legal education, regardless of the methodology. In the modern era, this goal has been denounced as too modest. As increasing numbers of beginning practitioners have discovered that they lack the practical skills they need, there has been an outcry from the bar for educational reform that has manifested itself in studies, commentaries and revised curriculi. The studies and commentaries call for supplemental training in analytical reasoning and opportunities for experiential education, including the use of new technology in the practice of law. The call for curricular changes expresses a professional need for new graduates to be practice ready and proficient. This Article explores the changing needs of the profession, the crisis in malpractice claims, the new demands being placed on legal academia, and the challenges faced by legal educators. The author's thesis is that the design and use of a decision tree for bankruptcy analysis can reduce or prevent professional error when used properly in clinical pedagogy.
A half century ago, the notion that decision tree software would guide a user through a series of questions and illuminate the critical issues in the legal analysis of a client's problem would have been considered science fiction. Now it is a reality, and the potential to harness this technology as an educational tool is ripe. This Article proposes the design and use of a decision tree algorithm that presents a data driven sequence of questions to guide the user towards an optimal recommendation regarding whether to file Chapter 7 consumer bankruptcy.
Prior to the advent of decision trees and other forms of branching logic, the essential questions related to legal analysis could be reduced to checklists or other written documents. Lawyers, judges, and professors memorized questions that were key to analyzing the most common legal problems through sheer repetition. In the digital age, attorneys can learn the same information with the added safeguard of a decision tree application that replicates the sequence of questions required in legal analysis.
The decision tree works well in the clinical setting because it is propositional. In other words, the decision tree is designed to introduce a logical series of propositions in the form of syllogisms. This format, represented as “if this, then that,” drives the process forward. The construction of the decision tree is designed to allow the sequence of questions to vary according to the responses chosen. The decision tree is conversational because its format engages users in an interactive exchange that directs the user towards a conclusion based on the responses supplied.
The uses and benefits of this decision tree technology in the clinical setting include: (1) protecting clients and students from professional error, (2) teaching students critical issues in legal analysis, (3) assisting clinical faculty in supervision, (4) improving risk management, (5) promoting access to justice, (6) fostering judicial economy, and (7) rehabilitating and reclaiming the image of lawyers as honored professionals. The prototype includes the questions to be posed, the universe of responses, and links to the relevant legal authority. The user's ability to analyze a given issue is either confirmed or corrected by the decision tree.
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