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How Specifications Grading Minimizes AI-Generated Responses in ASL: I use Pass/Fail specifications with live demonstration requirements for ASL assessments where students must meet clear criteria such as producing a short narrative, demonstrating the alphabet fluently, or viewing video clips of signers and answering comprehension questions through multiple-choice or fill-in-the-blank responses about what they observed. This minimizes AI misuse because it removes the "grade maximization" incentive and students are not chasing extra points that ChatGPT could help them earn and emphasizes authentic demonstration over polished scripted grammatical. The interactive activities (watching signed content and responding to comprehension questions, performing live with spontaneous follow-up questions) make it impossible to use AI effectively since ChatGPT cannot watch ASL videos or predict what specific signs appeared in the clips. Because reassessment is allowed with feedback, there's less pressure to "cheat" the first time; the focus shifts to actually learning the skill. Specifications grading naturally aligns with what ASL assessment should be demonstrating genuine visual-language comprehension and communicative competence making AI-generated content irrelevant rather than just forbidden.

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Specification Grading in History Courses

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Specification Grading in History Courses

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One way I could use Specifications Grading to reduce ChatGPT-generated responses is to create assignment specifications that require students to demonstrate their own research process and engagement with course materials, rather than simply submitting a polished written response. For example, in a history research assignment, specifications could require students to identify and analyze specific primary sources, explain why they selected each source, connect the sources to course readings, and provide a brief reflection explaining how their interpretation developed. This follows the case study’s emphasis on specifications that assess higher-level qualities such as application, analysis, and genuine engagement with the assignment.

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I have been trying to develop a similar system that was networking without the idea of pass/fail grading. I love the idea that not all students are on the same path and are interested in the same amount of work. I think in my classes it would make more sense to clearly outline less assignments to do quality work on for a set grade then poor work on all the assignments for the same grade.

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I want to like this, but just can't. It seems nice that you don't have to stress about whether to take off 1/2 a point here or there, but at the pass/fail threshold, your assessment is VERY consequential -- either telling them that the work they did needs re-doing or revising on one hand or is satisfactory on the other. This strong cutoff seems more objectionable (especially for essays or assignments that incorporate at least some subjective grading) and I would stress out more on those cutoffs than I do about awarding (or not) a point here and there.

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I've never used peer review because I've been worried about students being too harsh with their peers, but I think it allows them to practice evaluating as part of Bloom's taxonomy, which is incredibly beneficial. I think I will create sample feedback to encourage gentle, but constructive feedback.

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I'd really like to implement this in the future. It seems like it creates such clear objectives for students and creates a supportive framework rather than feeling limiting or constraining. It also encourages autonomy and innovation.

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I like specifications grading because it helps students follow rubrics and understand exactly why they received their scores. This way, they can identify their mistakes and improve on future assignments. It also allows for second attempts and includes a two-week grace period after the due date, which supports student learning and reduces stress.

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I have already flirted with the idea of using rubrics for my peer review. This is something I will expand upon.

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I like the idea of Activity Packages that utilize different modes of expression/application. A student may use AI to zhuzh up an assignment, but I'm imagining the conceptual understanding would come from him/her.

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Having a specific rubric for my art courses can help the student identify the target concepts and techniques for each project and how to begin to develop an understanding for meeting the standards for ongoing projects and grow the confidence to begin incorporating their own original ideas within artworks.

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I love this idea of listing specific requirements and expectations for an assignment. It goes beyond a prompt and acts like a rubric for the student to follow. It seems like the assignment would be easier to complete and lessen the potential dread.

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A project that makes using AI difficult within a specification grading system focuses on process-based, personalized, and iterative work rather than just final output. I currently stress the iterative nature of writing and have students turn in work at every stage of the writing porcess

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I think this spec grading system is very complex and interesting. The highly specific goal set up and opportunity for students to personalized their game plan to complete a project will promote student's engagement.

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Using Specifications Grading in Chinese Language Learning

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Using Specifications Grading in Chinese Language Learning

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One way specifications grading can help minimize ChatGPT-generated responses is by requiring students to meet specific, observable criteria tied to class engagement and personal expression. For example, in a writing assignment, I could specify that students must reference vocabulary or sentence patterns introduced in class, include a personal anecdote related to Chinese culture, and use at least one phrase from a recent class discussion.

These clear, customized criteria make it more difficult for students to submit generic or AI-generated responses and instead encourage authentic, course-connected work.

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implementing personal journey (lived experiences and observations of other people's lived experiences) as a major spec is crucial to me in averting AI and motivating students to rely on their original thoughts/ideas/words. I create multiple spaces, no-stakes spaces, for them to share personal journey and highlight that in graded assignments.

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Specifications-based grading presents a challenge for students attempting to rely on AI-generated responses. Because this approach emphasizes meeting clearly defined criteria through multiple drafts, personalized feedback, and ongoing revisions, it's not enough to simply paste content generated by AI. Instead, students must thoughtfully integrate feedback and demonstrate improvement over time, which requires a deeper understanding of the material and the ability to apply that understanding in specific, context-sensitive ways.

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Have students create a video where they self reflect on an assignment using the rubric as a guideline

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After an activity package is completed, use specifics grading so that student can self evaluate and determine how well they've ingested the packed material and concepts.

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Specs grading gives students so much agency as they can choose how much effort they want to put forth in order to earn the grade they want. So much choice motivates students to progress and they will likely resort to using AI less than in a traditional model.

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Revision opportunities are great for allowing the students time to absorb the material.

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There seems to be an emphasis on the grade rather than, is the student actually comprehending and absorbing the information. I do however like the opportunity for revisions.

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Specifications grading in a writing course minimizes AI-generated responses by requiring personalized narratives, peer reviews, and iterative revisions. This approach demands unique, reflective content and active engagement, making it difficult for AI to meet the tailored, detailed criteria and ensuring authentic student work.

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I allow for revisions in my course, but also use clear rubrics to explain why they got their grade in the first place; it lets me engage with dialogue with my students to figure out how they can improve. It's worth the extra time to allow for revisions within reasonable time limits (in my case, 2 weeks after the assignment's initial due date)!

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I like the design of the course described in the case study and would find this very engaging as a senior student, however this is very complex and would be inefficient to apply to a biology class of, often, non-major freshmen. To some students, it would be confusing beyond the point of them carrying. I have a hard time explaining the common point system at times. I think a specs system would be great, especially with some type of visual display of expectations, as presented in the case study, but it would need to be much simpler. It would benefit students who struggle with math and percentages.

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Allowing students to revise their work seems like a challenge in grading. I agree that it is important for students to recognize and learn from their mistakes, but at the same time I have students putting in minimal effort on the first attempt just to get the assignment turned in. It would be helpful to see how this can be applied to a science course effectively.

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The specifications model allows students to complete assignments or parts of assignments at different levels and times with personalized feedback to help them improve and move forward with learning. The scaffolding helps students connect material as they learn, and the feedback helps them revise or rethink assignments and information.

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Specifications grading gives students the opportunity to apply what they are learning to their own lives and not so much a requirement of memorization, but to help students engage in more personal reflection.

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In specifications grading, it is easier for students to gain mastery and understand application of concepts versus using traditional grading standards. It seems to encourage more deep reflection that might be a discouragement to using ChatGPT to generate answers.

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Genuine comprehension and application of knowledge

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Genuine comprehension and application of knowledge

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In the context of teaching, specifications grading can be instrumental in minimizing ChatGPT-generated responses by structuring assessments that specifically require critical thinking and authentic understanding.

Rather than traditional, broad questions that may be susceptible to automated responses, Spec grading includes assignments with clear specifications and criteria for success. For instance, we can use assignments that involve real-world applications or problem-solving scenarios, making it challenging for students to rely solely on ChatGPT for generic responses.

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Specs grading encourages assignments that need to be high quality without the pressure of perfection with the safety net to be able to redo assignments with consideration to instructor feedback. This promotes deep learning for students and allows faculty to spend more time focusing on feedback rather than grades.

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Utilizing specifications grading can mean providing multiple opportunities for students learn and gauge their knowledge of the content, rather than a singular assignment for assessment. Providing students with varied activities that focus on applying what they learned also gives weight to the varied learning styles as well.

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Allowing for revisions sounds overwhelming considering the number of assignments and students in my classes. However, by using clear rubrics along with a satisfactory/unsatisfactory grading system, it would allow for simplified grading and the opportunity for student revisions.

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This type of grading encourages students to generate their own personal ideas, and explain them in their own words. In order to meet the high criteria for passing, they must find a way to make the idea seem unique, coherent and convincing to the professor. This might require more than the more fact based content that Chat GPT typically provides.

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In specifications grading, you could require multiple drafts of an assignment, with each draft needing to incorporate specific, personalized feedback. This iterative process, focusing on improvement and depth of understanding, makes it challenging for students to rely on ChatGPT-generated responses. It encourages original thought and continuous engagement with the material.