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Pei Boon Ooi; Graeme Wilkinson – British Journal of Guidance & Counselling, 2025
The advent of generative Artificial Intelligence (AI) systems, such as large language model chatbots, is likely to have a significant impact in psychotherapy and counselling in the future. In this paper we consider the current state of AI in psychotherapy and counselling and the likely evolution of this field. We examine the ethical codes of…
Descriptors: Ethics, Artificial Intelligence, Governance, Computer Mediated Communication
Aaron Wolf – Educational Theory, 2025
Much has been written about how to improve the fairness of AI tools for decision-making but less has been said about how to approach this new field from the perspective of philosophy of education. My goal in this paper is to bring together criteria from the general algorithmic fairness literature with prominent values of justice defended by…
Descriptors: Algorithms, Artificial Intelligence, Technology Uses in Education, Educational Philosophy
Tamara Broderick; Andrew Gelman; Rachael Meager; Anna L. Smith; Tian Zheng – Grantee Submission, 2022
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond. To aid the development of trust in these decisions, we develop a taxonomy delineating where trust in an analysis can break down: (1) in the translation of real-world goals to goals on a particular set of training data, (2) in the…
Descriptors: Taxonomy, Trust (Psychology), Algorithms, Probability
Kil, David; Baldasare, Angela; Milliron, Mark – Current Issues in Education, 2021
Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'ĂȘtre of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy…
Descriptors: Learning Analytics, Academic Achievement, College Students, Electronic Learning
Grabinger, R. Scott – Performance and Instruction, 1988
This introductory article defines and delimits expert systems. The discussion covers the concepts of artificial intelligence, the components of an expert system, and the significance of expert systems when compared to more traditional decision making tools. (CLB)
Descriptors: Algorithms, Decision Making, Expert Systems, Heuristics
Harmon, Paul; Pipe, Peter – Performance and Instruction, 1986
Describes design and presents examples of industrial use of small expert systems and guidelines for choosing problems which lend themselves to small tool solutions. Use of microcomputer facilitated decision tables to diagnose and categorize people, things, and issues is suggested, and development of three decision table formats is described. (MBR)
Descriptors: Algorithms, Artificial Intelligence, Decision Making, Design
Peer reviewedO'Neill, Eileen S.; Dluhy, Nancy M. – Nursing Outlook, 2000
Describes structured care approaches, such as protocols, clinical pathways, and algorithms, which are being used increasingly to organize clinical knowledge and guide patient care. Reviews the types of structured care approaches available to the clinician and explores the value of structured care approaches in practice and education. (Contains 32…
Descriptors: Algorithms, Clinical Experience, Critical Path Method, Decision Making
Peer reviewedSong, Xueshu – International Journal of Instructional Media, 1992
Describes the development of a computer-aided decision-making system to assist educational practitioners in curriculum decision making about innovative program features and implementation requirements. Topics discussed include the identification of variables; database generation; mathematical modeling; compromising between effectiveness and…
Descriptors: Algorithms, Computer Software Development, Curriculum Development, Databases
Peer reviewedMurphy, Michael J.; And Others – Simulation and Games, 1982
Describes NEGOTIATE, an interactive computer model that synthesizes bargaining theories into algorithmic functions capable of simulating complex labor relations. The models employed in the simulation, the instructional dimension of NEGOTIATE, and various applications of the game are discussed. A seven-item reference list is included. (Author/JL)
Descriptors: Algorithms, Collective Bargaining, Computer Oriented Programs, Decision Making
Jonassen, David H.; Hannum, Wallace H. – Journal of Instructional Development, 1986
Describes functions comprising task analysis process--inventory, description, selection, sequencing, and analysis--and reviews distinctions between the micro/macro level, top-down/bottom-up, and job/learning task analysis processes. These functions and distinctions are combined into a quasi-algorithm suggesting which of 30 task analysis procedures…
Descriptors: Algorithms, Behavioral Objectives, Classification, Content Analysis
Straetmans, Gerard J. J. M.; Eggen, Theo J. H. M. – Educational Technology, 1998
Describes the workings of computerized adaptive testing (CAT). Focuses on the key concept of information and then discusses two important components of a CAT system: the calibrated item bank and the testing algorithm. Describes a CAT that was designed for making placement decisions on the basis of two typical test administrations and notes the…
Descriptors: Adaptive Testing, Algorithms, Computer Assisted Testing, Computer System Design
Terrell, William R.; And Others – Educational Technology, 1992
Explains human interactive analysis as an architecture for using computer interactive technologies in the analysis of complex work environments. A project at the Naval Training Systems Center that used video-audio data to develop a multimedia database is described; the analysis and management of data are discussed; and decision processes are…
Descriptors: Algorithms, Computer Assisted Instruction, Courseware, Data Analysis

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