{"id":119,"date":"2026-07-30T19:34:55","date_gmt":"2026-07-30T19:34:55","guid":{"rendered":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/?post_type=chapter&p=119"},"modified":"2026-09-09T14:13:15","modified_gmt":"2026-09-09T14:13:15","slug":"foundations","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/chapter\/foundations\/","title":{"rendered":"Introduction & Foundations"},"content":{"raw":"

Introduction: Why this Matters<\/h1>\r\nFor many faculty members on college campuses, a common word associated with AI is \u201cpressure.\u201d\u00a0 Pressure to keep up with advancing technology.\u00a0 Pressure to rethink how our courses should be designed to keep up as campus conditions shift.\u00a0 Pressure to keep pace with students arriving in our classrooms with more experience in using generative AI tools. Many instructors are being urged by university administration to reconsider how their courses fit within an evolving technological landscape.\r\n\r\nFor faculty who may be interested in how AI tools can inform their classes, course design is an obvious place to begin.\u00a0 Revising a course is slow, thoughtful work.\u00a0 The potential of AI as a design partner is not that it will completely automate course construction or infringe upon your autonomy as the owner of a course.\u00a0 Rather, AI tools can serve to reduce friction in the preliminary stages of course design.\u00a0 These tools can generate alternative approaches, stress-test classroom strategies, and identify pathways not previously considered.\r\n\r\nWhen used with thoughtful intention, AI tools can stand in for a kind of \u201cvirtual teaching assistant\u201d \u2013 one who requires oversight and direction but can support your thinking and decision-making.\r\n\r\nWe acknowledge concerns faculty have regarding implementing AI into academic work.\u00a0 AI can produce bias, flatten discourse and produce generic, unfocused structures when applied without critical attention.\u00a0 Furthermore, voices raising concern about student privacy, intellectual property and cognitive offload.\u00a0 Our skepticism is a demonstration of our responsibility to our profession; however, it should not be a bar against innovation.\r\n\r\nThis chapter is written by CSU faculty with a range of experience with using AI.\u00a0 We share many of the concerns faculty have as described above.\u00a0 Reading forward, in this chapter, we will share practical AI applications to course design that emphasize where AI can play a helpful role, and where human judgement must remain centered.\u00a0 But first, we would like to establish a set of shared concepts regarding AI in course design.\r\n

Foundations: Attitudes for Approaching AI as a Course Design Partner<\/h1>\r\n

Think of AI as a Course Design Partner (Not a Course Creator)<\/h2>\r\nA useful starting point is to think of generative AI as a helpful, if sometimes frustrating, assistant rather than an omniscient, omnipotent creator of courses. AI works best when tasked with quickly generating options (assignment ideas, draft learning outcomes, or ways to organize a unit, etc.) but it cannot know what best fits your course, discipline, or students.\r\n\r\nGen-AI\u2019s value, then, is not in automating a finished product ready to use out of the box, but in expanding the range of possibilities the operator (you) can consider.\r\n\r\nAn example: You might ask a CoPilot to suggest several ways to assess a learning outcome. All may seem reasonable, but only one may truly align with your course goals. The AI provides options; evaluating and selecting among them remains your responsibility.\r\n\r\nUsed this way, AI supports the design process, but instructor judgment remains at the center of course design. Your expertise (disciplinary knowledge, familiarity with your students, and sense of what counts as meaningful work) remains central to making those options usable.\r\n

Prioritize Iteration Over Output (Process Over Product)<\/h2>\r\nWhen using generative AI, be it to design a course or ask for a blueberry jame recipe, the name of the game is iteration and reiteration.\u00a0 The most useful results are never born from a single prompt (what is known as \u201cone-shotting\u201d). One-shot outputs are often broad, generic, or only partially aligned with the user\u2019s goals. This simply reflects how these AI systems are designed to generate plausible starting points rather than finished solutions.\r\n\r\nFor this reason, it is more productive to treat AI interaction as an iterative process rather than a one-shot transaction. Each AI response becomes something you can refine\u2014by narrowing the task, adding disciplinary context, or specifying constraints\u2014so that the output becomes more relevant to your course.\r\n\r\nAn example: An initial prompt might produce a general discussion assignment: a good starting point, but not something that\u2019s ready to post on the class discussion board. Following up with further, iterative prompts that specify your course level, desired learning outcome, and disciplinary expectations will typically produce something more usable.\u00a0 This process can be repeated again and again until the desired output is achieved.\r\n\r\nThe final assignment comes not from a perfect first answer, but from the sequence of revisions that follow.\r\n\r\nUsed this way, AI supports a cycle of drafting, reviewing, and refining. Your role is to guide that process, using your expertise to shape outputs into something that fits your specific teaching context.\r\n

Practice Constructive Skepticism<\/h2>\r\nIt is our opinion that the best approach to incorporating AI tools in course design is to practice constructive skepticism.\u00a0 This means treating the AI as provisional \u2013 something to question, mold and align with your goals \u2013 rather than outright accepting or rejecting its output.\u00a0 This human-centered approach is what is most appropriate in preserving the integrity of our pedagogy and best serving our discipline and students.\r\n\r\nThis balance becomes especially important when completing multi-tiered, complex tasks such as course design where alignment across course goals, learning outcomes, assignment and assessments is essential.\u00a0 Human oversight of AI output is critical and if we\u2019re going to maximize this technology\u2019s potential, we must analyze and evaluate generative AI\u2019s output with a critical, unflinching eye.\r\n\r\nIn the remainder of this chapter, we will discuss practical applications of generative AI in course design.\u00a0 This includes insights and shared experiences from CSU faculty on their experiences utilizing GenAI to redesign courses, co-create lesson plans to meet learning outcomes.\u00a0 We will see step-by-step examples of course design and practical strategies and considerations to consider before using GenAI in course design.\u00a0 Finally, we will consider potential pitfalls to implementing this technology in our classrooms.","rendered":"

Introduction: Why this Matters<\/h1>\n

For many faculty members on college campuses, a common word associated with AI is \u201cpressure.\u201d\u00a0 Pressure to keep up with advancing technology.\u00a0 Pressure to rethink how our courses should be designed to keep up as campus conditions shift.\u00a0 Pressure to keep pace with students arriving in our classrooms with more experience in using generative AI tools. Many instructors are being urged by university administration to reconsider how their courses fit within an evolving technological landscape.<\/p>\n

For faculty who may be interested in how AI tools can inform their classes, course design is an obvious place to begin.\u00a0 Revising a course is slow, thoughtful work.\u00a0 The potential of AI as a design partner is not that it will completely automate course construction or infringe upon your autonomy as the owner of a course.\u00a0 Rather, AI tools can serve to reduce friction in the preliminary stages of course design.\u00a0 These tools can generate alternative approaches, stress-test classroom strategies, and identify pathways not previously considered.<\/p>\n

When used with thoughtful intention, AI tools can stand in for a kind of \u201cvirtual teaching assistant\u201d \u2013 one who requires oversight and direction but can support your thinking and decision-making.<\/p>\n

We acknowledge concerns faculty have regarding implementing AI into academic work.\u00a0 AI can produce bias, flatten discourse and produce generic, unfocused structures when applied without critical attention.\u00a0 Furthermore, voices raising concern about student privacy, intellectual property and cognitive offload.\u00a0 Our skepticism is a demonstration of our responsibility to our profession; however, it should not be a bar against innovation.<\/p>\n

This chapter is written by CSU faculty with a range of experience with using AI.\u00a0 We share many of the concerns faculty have as described above.\u00a0 Reading forward, in this chapter, we will share practical AI applications to course design that emphasize where AI can play a helpful role, and where human judgement must remain centered.\u00a0 But first, we would like to establish a set of shared concepts regarding AI in course design.<\/p>\n

Foundations: Attitudes for Approaching AI as a Course Design Partner<\/h1>\n

Think of AI as a Course Design Partner (Not a Course Creator)<\/h2>\n

A useful starting point is to think of generative AI as a helpful, if sometimes frustrating, assistant rather than an omniscient, omnipotent creator of courses. AI works best when tasked with quickly generating options (assignment ideas, draft learning outcomes, or ways to organize a unit, etc.) but it cannot know what best fits your course, discipline, or students.<\/p>\n

Gen-AI\u2019s value, then, is not in automating a finished product ready to use out of the box, but in expanding the range of possibilities the operator (you) can consider.<\/p>\n

An example: You might ask a CoPilot to suggest several ways to assess a learning outcome. All may seem reasonable, but only one may truly align with your course goals. The AI provides options; evaluating and selecting among them remains your responsibility.<\/p>\n

Used this way, AI supports the design process, but instructor judgment remains at the center of course design. Your expertise (disciplinary knowledge, familiarity with your students, and sense of what counts as meaningful work) remains central to making those options usable.<\/p>\n

Prioritize Iteration Over Output (Process Over Product)<\/h2>\n

When using generative AI, be it to design a course or ask for a blueberry jame recipe, the name of the game is iteration and reiteration.\u00a0 The most useful results are never born from a single prompt (what is known as \u201cone-shotting\u201d). One-shot outputs are often broad, generic, or only partially aligned with the user\u2019s goals. This simply reflects how these AI systems are designed to generate plausible starting points rather than finished solutions.<\/p>\n

For this reason, it is more productive to treat AI interaction as an iterative process rather than a one-shot transaction. Each AI response becomes something you can refine\u2014by narrowing the task, adding disciplinary context, or specifying constraints\u2014so that the output becomes more relevant to your course.<\/p>\n

An example: An initial prompt might produce a general discussion assignment: a good starting point, but not something that\u2019s ready to post on the class discussion board. Following up with further, iterative prompts that specify your course level, desired learning outcome, and disciplinary expectations will typically produce something more usable.\u00a0 This process can be repeated again and again until the desired output is achieved.<\/p>\n

The final assignment comes not from a perfect first answer, but from the sequence of revisions that follow.<\/p>\n

Used this way, AI supports a cycle of drafting, reviewing, and refining. Your role is to guide that process, using your expertise to shape outputs into something that fits your specific teaching context.<\/p>\n

Practice Constructive Skepticism<\/h2>\n

It is our opinion that the best approach to incorporating AI tools in course design is to practice constructive skepticism.\u00a0 This means treating the AI as provisional \u2013 something to question, mold and align with your goals \u2013 rather than outright accepting or rejecting its output.\u00a0 This human-centered approach is what is most appropriate in preserving the integrity of our pedagogy and best serving our discipline and students.<\/p>\n

This balance becomes especially important when completing multi-tiered, complex tasks such as course design where alignment across course goals, learning outcomes, assignment and assessments is essential.\u00a0 Human oversight of AI output is critical and if we\u2019re going to maximize this technology\u2019s potential, we must analyze and evaluate generative AI\u2019s output with a critical, unflinching eye.<\/p>\n

In the remainder of this chapter, we will discuss practical applications of generative AI in course design.\u00a0 This includes insights and shared experiences from CSU faculty on their experiences utilizing GenAI to redesign courses, co-create lesson plans to meet learning outcomes.\u00a0 We will see step-by-step examples of course design and practical strategies and considerations to consider before using GenAI in course design.\u00a0 Finally, we will consider potential pitfalls to implementing this technology in our classrooms.<\/p>\n","protected":false},"author":3,"menu_order":1,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":["jkane"],"pb_section_license":""},"chapter-type":[],"contributor":[83],"license":[],"class_list":["post-119","chapter","type-chapter","status-publish","hentry","contributor-jkane"],"part":110,"_links":{"self":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters\/119","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":7,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters\/119\/revisions"}],"predecessor-version":[{"id":263,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapters\/119\/revisions\/263"}],"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\/119\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/media?parent=119"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/pressbooks\/v2\/chapter-type?post=119"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/contributor?post=119"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.ulib.csuohio.edu\/usingaiinacademics\/wp-json\/wp\/v2\/license?post=119"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}