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Enhancing Expertise, Sociability, and Literacy through Teaching Artificial Intelligence as a Lab Science

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2012 ASEE Annual Conference & Exposition


San Antonio, Texas

Publication Date

June 10, 2012

Start Date

June 10, 2012

End Date

June 13, 2012



Conference Session

NSF Grantees' Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count


Page Numbers

25.569.1 - 25.569.11



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Paper Authors


Stephanie Elizabeth August Loyola Marymount University

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Stephanie August is an Associate Professor and Special Assistant to the Chief Academic Officer for Graduate Education at Loyola Marymount University, Los Angeles. She teaches courses in artificial intelligence, database management systems, and software engineering. Her research interests include applications of artificial intelligence including interdisciplinary new media applications, natural language understanding, argumentation, and analogical reasoning. She has several publications in these areas. August is actively involved in the scholarship of teaching and learning community and is a 2006 CASTL Institute Scholar (Carnegie Academy for the Scholarship of Teaching and Learning). She is currently directing graduate and undergraduate students on two NSF-funded projects, to develop materials for teaching artificial intelligence through an experimental approach modeled after the lab sciences, and to develop a Virtual Engineering Sciences Learning Lab in Second Life to provide an immersive learning environment for introductory engineering and computer science courses. Her industry experience includes software and system engineering for several defense C3I programs, and applied artificial intelligence research for military and medical applications.

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Enhancing Expertise, Sociability and Literacy through Teaching Artificial Intelligence as a Lab ScienceThe Teaching Artificial Intelligence as a Laboratory Science (TAILS) project is designed todevelop a new paradigm for teaching introductory artificial intelligence (AI) concepts byimplementing an experimental approach modeled after the lab sciences. It explores whetherstructured labs with exercises that are completed in teams before students leave the classroomcan build a sense of accomplishment, confidence, community, and collaboration among students,characteristics which have been shown to be critical to retain women and non-traditionalcomputer science students in the field.TAILS presents to students an array of fundamental AI algorithms as a set of hands-on activitiesmade available through a database of lab activities, including software exercises and experimentsthat provide experience with concepts from multiple perspectives and multiple modes ofrepresentation. Best practices in software engineering will be reinforced in students throughcareful design and documentation of the modulesThe proposed activities are designed to engage the kinetic learner and provide the “big picture”that model-driven learners need to assimilate course material. Existing research has shown thatstructured labs with exercises that can be completed before students leave the classroom build asense of accomplishment and confidence. Progressively sophisticated experiments teachinexperienced students and challenge more advanced students.TAILS contributes two components to STEM education: a set of lab experiments to promotestudent retention of concepts and retention of majors, and insight into student learning throughthe labs. TAILS contributes to exemplary STEM education by creating learning materials andstrategies, implementing new instructional strategies, and assessing and evaluating studentThis project addresses learning outcomes in five categories: skills (students will demonstrateachievement.the ability to solve problems collaboratively as they work in pairs to complete labactivities), concepts (students will demonstrate knowledge of artificial intelligence andsoftware engineering concepts), communication (students will be able to describe courseconcepts at multiple levels of abstraction), application (students will be able to identifyapplications of AI concepts), and research (students will demonstrate curiosity aboutcourse material). Assessments will draw from standard classroom assessments described byAngelo and Cross (1993) and the Online Evaluation Resource Library. They will include inpart a teamwork attitude questionnaire, a team process log to record perceptions aboutcollaboration, exam questions and pre- and post-tests, explanation and implementation ofsoftware code, concept maps, contrasts of multiple concepts, specification of requirementsfor a software program , domain- and implementation-level design of software programs,descriptions of algorithms geared toward non-computer scientists and technical managers,application cards, explanations of the objectives and significance of experiments, andenhancement of algorithms.TAILS contributes to the STEM education knowledge base by promoting individual efforts tosolve a programming assignment while building an education community through laboratorywork that encourages cooperation and teamwork among students. The paradigm can be adaptedto computer science courses at all academic levels and is expected to increase participation in thefield by shortening the time required to prepare undergraduates to engage in research.This paper describes the nature of this project and presents preliminary results.ReferencesAngelo, Thomas A. and Cross, K. Patricia. Classroom Assessment Techniques; A Handbook for College Teachers. 2nd edition. San Francisco: Jossey-Bass, 1993.OERL: Online Evaluation Resource Library. (last accessed 7October 2011)

August, S. E. (2012, June), Enhancing Expertise, Sociability, and Literacy through Teaching Artificial Intelligence as a Lab Science Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21326

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