AI Learning Target Generator for Higher Education
Higher education learning outcomes live in two registers: the course-level Student Learning Outcomes (SLOs) that appear in accreditation documents and the lesson-level targets that guide what students do in each class meeting. Most college instructors write strong SLOs but never translate them into the daily I-can statements that help students self-regulate their learning within a single lecture or seminar session. The AI Learning Target Generator helps bridge this gap, with lesson-level targets and success criteria from any course topic, which the instructor aligns to the SLO and the rubric students will encounter at the end of the unit.
This page fills in a draft from a template. Copy prompt gives you the prompt for any AI chat.
How college and university instructors use it
Classroom workflows that show how it can work.
Dr. Martinez's introduction to sociology seminar target
Dr. Martinez is teaching a seminar on social stratification. The course SLO reads: 'Students will apply sociological theories to analyze contemporary social inequality.' For a single class session on Bourdieu, she enters the SLO and the day's topic. The AI generates a lesson-level target: 'I can apply Bourdieu's concepts of cultural and social capital to explain why students from different social backgrounds have different educational outcomes.' Asked in the Topic field, it also writes three measurable criteria: '(1) I can define cultural capital and give a concrete example from education. (2) I can connect social capital to network effects in college admissions. (3) I can make a claim about which form of capital better predicts educational attainment and defend it with evidence.' Dr. Martinez posts this target and uses the criteria to grade the seminar participation rubric.
Prof. Williams's first-year writing course daily target
Prof. Williams teaches first-year composition. For a lesson on integrating sources, the course SLO is 'Compose arguments that integrate multiple sources with appropriate attribution.' The AI generates a lesson target: 'I can integrate a source quotation into a paragraph by introducing it, quoting it, and explaining how it supports my claim, not just dropping it in.' Success criteria: '(1) I can write a signal phrase that introduces the source and its credibility. (2) I can quote only the most relevant portion of the source. (3) I can write a follow-on sentence that explains how the quote supports my specific argument.' Students bring a draft paragraph and self-assess against these criteria during peer review.
Dr. Patel's organic chemistry lab learning target
Dr. Patel runs an undergraduate organic chemistry lab. For a synthesis lab, the program competency is 'Design and execute multi-step organic syntheses.' The AI generates the pre-lab target: 'I can predict the mechanism and expected product for a two-step synthesis and identify the safety considerations for each reagent.' Asked in the Topic field, it also writes four criteria: '(1) I can draw the expected mechanism for each reaction step. (2) I can predict the stereochemical outcome and explain my reasoning. (3) I can identify the hazard class of each reagent. (4) I can state the expected yield range and explain what side reactions could reduce it.' Students submit a pre-lab report using these criteria as the outline.
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What this page does here
This page builds a draft from a template on this device. Copy prompt gives you the prompt for any AI chat.
Questions teachers ask
How does the generator align lesson-level targets to course-level SLOs for accreditation?
The tool keeps no alignment records: it names the standard or SLO you enter on the draft, and the instructor keeps the link from lesson target to course SLO to program outcome. When you enter a course SLO and a specific topic, the draft shows the SLO beside the targets, and the instructor notes which aspect of it the lesson addresses.
Can the generator produce measurable learning objectives in Bloom's taxonomy terms for syllabi?
Yes. The AI writes student-facing 'I can' statements with measurable verbs and names the Bloom's level of each, and you can reword them as formal objectives for a syllabus or course planning document: 'Students will analyze...,' 'Students will evaluate...,' 'Students will construct...' These differ from the student-facing I-can targets used in class sessions, they are written in the formal third-person academic register appropriate for a course syllabus, assessment blueprint, or accreditation self-study document.
How do higher education learning targets differ from K-12 targets in format and function?
Higher education targets are typically more open-ended and less procedurally scaffolded than K-12 targets, reflecting the greater autonomy expected of college students. A K-12 success criterion specifies each step explicitly; a higher education success criterion describes the quality of reasoning the student demonstrates in performing the step. College targets also frequently address metacognitive skills ('I can identify the limits of my own analysis and explain what additional evidence would strengthen it') that are rarely the explicit focus of K-12 learning targets.
Can I use this for graduate-level seminar and research course targets?
Yes. With graduate level named in the Topic field, targets can be generated at the creation and evaluation tiers of Bloom's taxonomy, and they can address disciplinary epistemology rather than content alone: 'I can evaluate the methodological choices in a primary research article and explain how they constrain the conclusions the authors can draw.' For research skills courses, targets can describe the specific research competency the session develops (database search strategy, qualitative coding, statistical modeling decision) with success criteria that describe expert-level performance.
Related tools
AI with your school's own student and course data is part of OpenEduCat.
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