{"id":127,"date":"2026-07-30T19:36:29","date_gmt":"2026-07-30T19:36:29","guid":{"rendered":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/?post_type=chapter&#038;p=127"},"modified":"2026-08-14T00:53:03","modified_gmt":"2026-08-14T00:53:03","slug":"case-study-redesigning-an-undergraduate-dynamics-course-using-generative-artificial-intelligence","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/chapter\/case-study-redesigning-an-undergraduate-dynamics-course-using-generative-artificial-intelligence\/","title":{"rendered":"Case Study:  Redesigning an Undergraduate Dynamics Course Using Generative Artificial Intelligence"},"content":{"raw":"<h1>Introduction<\/h1>\r\nDynamics is a foundational course in the undergraduate mechanical engineering curriculum. Students are introduced to the analysis of particle and rigid-body motion, force and acceleration relationships, work-energy methods, and impulse-momentum principles. Despite its importance, many students struggle with the transition from statics to dynamics due to the increased mathematical complexity and the need to visualize motion. Generative Artificial Intelligence (AI) offers opportunities to redesign Dynamics courses by enhancing course planning, content development, assessment creation, and student learning support.\r\n\r\nThis paper describes how generative AI can be incorporated throughout the lifecycle of an undergraduate Dynamics course while maintaining academic rigor and engineering judgment.\r\n<h1>Course Planning and Syllabus Development<\/h1>\r\nGenerative AI can assist instructors in developing measurable course outcomes aligned with program and accreditation requirements. AI tools can generate draft learning objectives, prerequisite knowledge maps, and topic sequencing plans that instructors can refine.\r\n\r\nFor example, AI can assist in creating outcomes such as:\r\n<ul>\r\n \t<li>Analyze the motion of particles and rigid bodies.<\/li>\r\n \t<li>Apply Newton\u2019s laws to dynamic systems.<\/li>\r\n \t<li>Use work-energy and impulse-momentum methods to solve engineering problems.<\/li>\r\n \t<li>Interpret and communicate engineering results effectively.<\/li>\r\n<\/ul>\r\nAI can also generate a complete syllabus, including course descriptions, grading policies, weekly schedules, and learning resources. Faculty members remain responsible for ensuring alignment with departmental standards and institutional policies.\r\n<h1>Instructional Material Development<\/h1>\r\nOne of the most valuable uses of AI is the creation of instructional materials. Instructors can generate lecture outlines, summaries, examples, and visual explanations for difficult concepts.\r\n\r\nExamples include:\r\n<ul>\r\n \t<li>Projectile motion problems.<\/li>\r\n \t<li>Curvilinear motion examples.<\/li>\r\n \t<li>Rotational dynamics applications.<\/li>\r\n \t<li>Vehicle acceleration and braking analyses.<\/li>\r\n \t<li>Robotic arm motion studies.<\/li>\r\n<\/ul>\r\nAI can produce multiple versions of worked examples at different difficulty levels, allowing instructors to address diverse student backgrounds. In addition, AI-generated visualizations can help students understand velocity vectors, acceleration components, and relative motion concepts that are often difficult to visualize using static textbook figures.\r\n<h1>Active Learning and Classroom Engagement<\/h1>\r\nGenerative AI can support active learning strategies by rapidly creating in-class exercises and discussion prompts.\r\n\r\nFor example, instructors can request:\r\n<ul>\r\n \t<li>Conceptual questions targeting misconceptions.<\/li>\r\n \t<li>Real-world engineering scenarios.<\/li>\r\n \t<li>Think-pair-share activities.<\/li>\r\n \t<li>Group problem-solving exercises.<\/li>\r\n<\/ul>\r\nA Dynamics instructor might use AI to generate a scenario involving the motion of a delivery drone or an autonomous vehicle and ask students to identify applicable principles before solving the problem mathematically.\r\n\r\nAI can also generate alternative versions of classroom activities, reducing the likelihood of students simply sharing solutions.\r\n<h1>Homework and Assessment Design<\/h1>\r\nAssessment development is traditionally one of the most time-consuming aspects of teaching. AI can assist by generating homework sets, quizzes, and examinations aligned with specific learning outcomes.\r\n\r\nExamples include:\r\n<ul>\r\n \t<li>Multiple versions of homework problems with different parameters.<\/li>\r\n \t<li>Conceptual quiz questions.<\/li>\r\n \t<li>Practice examinations with solutions.<\/li>\r\n \t<li>Rubrics for open-ended problems.<\/li>\r\n<\/ul>\r\nFaculty members must review all AI-generated content to verify technical accuracy and ensure appropriate levels of difficulty.\r\n\r\nAI can also assist in generating assessment questions across multiple cognitive levels, ranging from basic application of equations to higher-level analysis and engineering decision making.\r\n<h1>Student Support and Personalized Learning<\/h1>\r\nStudents increasingly use generative AI as an on-demand tutor. When used appropriately, AI can provide additional explanations, step-by-step guidance, and practice opportunities outside the classroom.\r\n\r\nStudents may ask AI to:\r\n<ul>\r\n \t<li>Explain relative motion concepts.<\/li>\r\n \t<li>Demonstrate alternative solution methods.<\/li>\r\n \t<li>Generate additional practice problems.<\/li>\r\n \t<li>Review problem-solving procedures.<\/li>\r\n<\/ul>\r\nThis personalized support can improve student confidence and reduce frustration when working independently.\r\n\r\nHowever, students should be trained to critically evaluate AI-generated solutions because computational and conceptual errors can occur.\r\n<h1>Responsible AI Use<\/h1>\r\nA redesigned Dynamics course should explicitly address responsible AI use. Students should be encouraged to use AI as a learning aid rather than a solution generator.\r\n\r\nAppropriate uses include:\r\n<ul>\r\n \t<li>Concept clarification.<\/li>\r\n \t<li>Study assistance.<\/li>\r\n \t<li>Practice problem generation.<\/li>\r\n \t<li>Programming support.<\/li>\r\n<\/ul>\r\nInappropriate uses include:\r\n<ul>\r\n \t<li>Submitting AI-generated solutions without verification.<\/li>\r\n \t<li>Bypassing problem-solving processes.<\/li>\r\n \t<li>Misrepresenting AI-generated work as original work.<\/li>\r\n<\/ul>\r\nEngineering judgment and validation should remain central components of student learning.\r\n<h1>Continuous Course Improvement<\/h1>\r\nAt the end of each semester, AI can assist instructors in analyzing student performance data, identifying difficult topics, and summarizing course evaluations.\r\n\r\nPatterns in student errors can help instructors revise instructional materials, adjust pacing, and improve future assessments. This data-driven approach supports continuous improvement while reducing faculty workload.\r\n<h1>Conclusion<\/h1>\r\nGenerative AI provides significant opportunities for redesigning undergraduate Dynamics courses. Applications include syllabus development, instructional material creation, active learning support, assessment generation, personalized tutoring, and continuous course improvement. When used responsibly, AI can enhance student engagement and learning while allowing instructors to focus on higher-value educational activities. The most effective implementation views AI as a collaborative educational tool rather than a replacement for instructor expertise","rendered":"<h1>Introduction<\/h1>\n<p>Dynamics is a foundational course in the undergraduate mechanical engineering curriculum. Students are introduced to the analysis of particle and rigid-body motion, force and acceleration relationships, work-energy methods, and impulse-momentum principles. Despite its importance, many students struggle with the transition from statics to dynamics due to the increased mathematical complexity and the need to visualize motion. Generative Artificial Intelligence (AI) offers opportunities to redesign Dynamics courses by enhancing course planning, content development, assessment creation, and student learning support.<\/p>\n<p>This paper describes how generative AI can be incorporated throughout the lifecycle of an undergraduate Dynamics course while maintaining academic rigor and engineering judgment.<\/p>\n<h1>Course Planning and Syllabus Development<\/h1>\n<p>Generative AI can assist instructors in developing measurable course outcomes aligned with program and accreditation requirements. AI tools can generate draft learning objectives, prerequisite knowledge maps, and topic sequencing plans that instructors can refine.<\/p>\n<p>For example, AI can assist in creating outcomes such as:<\/p>\n<ul>\n<li>Analyze the motion of particles and rigid bodies.<\/li>\n<li>Apply Newton\u2019s laws to dynamic systems.<\/li>\n<li>Use work-energy and impulse-momentum methods to solve engineering problems.<\/li>\n<li>Interpret and communicate engineering results effectively.<\/li>\n<\/ul>\n<p>AI can also generate a complete syllabus, including course descriptions, grading policies, weekly schedules, and learning resources. Faculty members remain responsible for ensuring alignment with departmental standards and institutional policies.<\/p>\n<h1>Instructional Material Development<\/h1>\n<p>One of the most valuable uses of AI is the creation of instructional materials. Instructors can generate lecture outlines, summaries, examples, and visual explanations for difficult concepts.<\/p>\n<p>Examples include:<\/p>\n<ul>\n<li>Projectile motion problems.<\/li>\n<li>Curvilinear motion examples.<\/li>\n<li>Rotational dynamics applications.<\/li>\n<li>Vehicle acceleration and braking analyses.<\/li>\n<li>Robotic arm motion studies.<\/li>\n<\/ul>\n<p>AI can produce multiple versions of worked examples at different difficulty levels, allowing instructors to address diverse student backgrounds. In addition, AI-generated visualizations can help students understand velocity vectors, acceleration components, and relative motion concepts that are often difficult to visualize using static textbook figures.<\/p>\n<h1>Active Learning and Classroom Engagement<\/h1>\n<p>Generative AI can support active learning strategies by rapidly creating in-class exercises and discussion prompts.<\/p>\n<p>For example, instructors can request:<\/p>\n<ul>\n<li>Conceptual questions targeting misconceptions.<\/li>\n<li>Real-world engineering scenarios.<\/li>\n<li>Think-pair-share activities.<\/li>\n<li>Group problem-solving exercises.<\/li>\n<\/ul>\n<p>A Dynamics instructor might use AI to generate a scenario involving the motion of a delivery drone or an autonomous vehicle and ask students to identify applicable principles before solving the problem mathematically.<\/p>\n<p>AI can also generate alternative versions of classroom activities, reducing the likelihood of students simply sharing solutions.<\/p>\n<h1>Homework and Assessment Design<\/h1>\n<p>Assessment development is traditionally one of the most time-consuming aspects of teaching. AI can assist by generating homework sets, quizzes, and examinations aligned with specific learning outcomes.<\/p>\n<p>Examples include:<\/p>\n<ul>\n<li>Multiple versions of homework problems with different parameters.<\/li>\n<li>Conceptual quiz questions.<\/li>\n<li>Practice examinations with solutions.<\/li>\n<li>Rubrics for open-ended problems.<\/li>\n<\/ul>\n<p>Faculty members must review all AI-generated content to verify technical accuracy and ensure appropriate levels of difficulty.<\/p>\n<p>AI can also assist in generating assessment questions across multiple cognitive levels, ranging from basic application of equations to higher-level analysis and engineering decision making.<\/p>\n<h1>Student Support and Personalized Learning<\/h1>\n<p>Students increasingly use generative AI as an on-demand tutor. When used appropriately, AI can provide additional explanations, step-by-step guidance, and practice opportunities outside the classroom.<\/p>\n<p>Students may ask AI to:<\/p>\n<ul>\n<li>Explain relative motion concepts.<\/li>\n<li>Demonstrate alternative solution methods.<\/li>\n<li>Generate additional practice problems.<\/li>\n<li>Review problem-solving procedures.<\/li>\n<\/ul>\n<p>This personalized support can improve student confidence and reduce frustration when working independently.<\/p>\n<p>However, students should be trained to critically evaluate AI-generated solutions because computational and conceptual errors can occur.<\/p>\n<h1>Responsible AI Use<\/h1>\n<p>A redesigned Dynamics course should explicitly address responsible AI use. Students should be encouraged to use AI as a learning aid rather than a solution generator.<\/p>\n<p>Appropriate uses include:<\/p>\n<ul>\n<li>Concept clarification.<\/li>\n<li>Study assistance.<\/li>\n<li>Practice problem generation.<\/li>\n<li>Programming support.<\/li>\n<\/ul>\n<p>Inappropriate uses include:<\/p>\n<ul>\n<li>Submitting AI-generated solutions without verification.<\/li>\n<li>Bypassing problem-solving processes.<\/li>\n<li>Misrepresenting AI-generated work as original work.<\/li>\n<\/ul>\n<p>Engineering judgment and validation should remain central components of student learning.<\/p>\n<h1>Continuous Course Improvement<\/h1>\n<p>At the end of each semester, AI can assist instructors in analyzing student performance data, identifying difficult topics, and summarizing course evaluations.<\/p>\n<p>Patterns in student errors can help instructors revise instructional materials, adjust pacing, and improve future assessments. This data-driven approach supports continuous improvement while reducing faculty workload.<\/p>\n<h1>Conclusion<\/h1>\n<p>Generative AI provides significant opportunities for redesigning undergraduate Dynamics courses. Applications include syllabus development, instructional material creation, active learning support, assessment generation, personalized tutoring, and continuous course improvement. When used responsibly, AI can enhance student engagement and learning while allowing instructors to focus on higher-value educational activities. The most effective implementation views AI as a collaborative educational tool rather than a replacement for instructor expertise<\/p>\n","protected":false},"author":3,"menu_order":7,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":["enabiyouni"],"pb_section_license":""},"chapter-type":[],"contributor":[61],"license":[],"class_list":["post-127","chapter","type-chapter","status-publish","hentry","contributor-enabiyouni"],"part":110,"_links":{"self":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters\/127","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/users\/3"}],"version-history":[{"count":3,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters\/127\/revisions"}],"predecessor-version":[{"id":215,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters\/127\/revisions\/215"}],"part":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/parts\/110"}],"metadata":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters\/127\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/media?parent=127"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapter-type?post=127"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/contributor?post=127"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/license?post=127"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}