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.

The decision tree is accessible from any location because it is available online. It can also be downloaded and used in a situation where internet access is unavailable. The system does not require encryption because client identifiers are unnecessary, although encryption can be added as a precautionary feature. The components of the system can be updated as the law changes because it is resident on a local server. The integrity of the system is protected by firewalls. Preservation of the accuracy of the analysis is ensured through the use of daily electronic search queries that retrieve any changes in the law that alter the decision tree analysis.

The automated search queries are matched to corresponding cells in the tree that contain the relevant legal citation. The queries are formulated to search all databases containing statutes, rules of procedure, administrative rules, rules of ethics, cases, and other critical information. The database management process is a ten-step procedure that can be repeated at regular intervals. The information from the searches reveal both when the decision tree should be changed and how it should be changed. The research team assigned to maintain and update the decision tree uses the search results to make any necessary corrections.

The choice of the bankruptcy subject area for the prototype was purposeful. Many issues in bankruptcy are numbers driven, including decisions that relate to property exemptions, income and expense information, time calculations related to venue, eligibility to file, and determining what exemptions are available. Other issues are susceptible to yes or no answers, or to a limited range of responses. This distinguishes bankruptcy analysis from many other areas of the law with issues that relate to concepts that are less clearly defined, such as foreseeability and reasonableness.

Bankruptcy is an ideal subject area for the decision tree model because it is in the top five practice areas for malpractice claims, and because “failure to know and apply the law” is the top category for claims by type of alleged error.10 Finally, bankruptcy filings remain high in some states, and even continue to rise in one jurisdiction.12 When unemployment is high, many consumers do not have the means to pay their debt and mortgage obligations. During the Great Recession, escalating medical costs, and record foreclosures created a surge in Chapter 7 filings.

This Article will address the positive impact of using the decision tree model in four parts. Part I will provide a historical overview of the evolution of legal education and the profession’s call for more experiential education, both generally and specifically, through clinical training and the use of technology. This Section will provide context and argue that the use of decision trees in the clinical setting is the natural culmination of the legal academy’s goals of teaching analytical skills, preparing graduates for practice, and incorporating new technology into the practice of law.

Part II will describe the legal malpractice problem in the United States and furnish the statistical data that serves as a call to arms against the epidemic of preventable malpractice. This Section focuses on the major categories of malpractice claims by area of practice, firm size, disposition of claims, types of alleged error, defense expenses paid, indemnity dollars paid to claimant, and time interval from date of error to closing of claim file. The Section shows how the decision tree technology can reduce professional error and, consequently, exposure to professional liability.

Part III will examine the other uses and benefits of the decision tree in clinical pedagogy and show how the use of a decision tree can indoctrinate students in the law, assist in the supervision of students, improve risk management, promote access to justice, foster judicial economy, and help reclaim the image of lawyers as respected professionals.

Part IV will highlight the steps in the design and construction of a specialized decision tree, illustrating the functionality of the system. It will provide an example of the hands-on approach of the decision tree that allows users to visualize the questions, potential responses, and citations of authority in a logical sequence.

The prototype decision tree has branching modules that relate to four pre-filing considerations: filing eligibility; dischargeability of debt, including global objections to discharge and exceptions to discharge; exemption eligibility, including determination of applicable jurisdiction, categorical eligibility, and limitations on dollar amount; and self-incrimination. Appendix A provides a visual depiction of how the online system looks and functions. Appendix B illustrates the data management process of updating the tree. Each module remains a work-in-process because of the need for constant monitoring and updating.

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