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CriterionNot Started (0)Developing/Emerging (1)Foundational (2)Skilled (3)Expert (4)
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1. Name: Completeness of Portfolio Deliverables
Description: Learner submits a single Future-Ready Portfolio PDF containing all nine Section 1 deliverables (Audience & Goal Frame, LinkedIn Profile mockup with two-model comparison, Reflective Statement to Manager, AI Productivity Pitch, Reflection Image, LinkedIn Reflection Post, Reflective Discussion Post, Retargeting Note, Personal AI Line) and all three Section 2 components (Prompt & Iteration Log, Process Documentation, Final Reflection). Any optional live-profile or post links are not assessed — everything graded must appear as text or images inside the PDF itself.
Source: Generative AI: Fundamentals to Future-Ready — MAS Brief: entire 'What to Submit' section (Sections 1 and 2). Evolved from v1 criterion 'Component Inclusion and Completeness'.		No submission is provided, or the PDF shows no meaningful attempt at the required portfolio structure.• Several components are attempted but most fall short of completeness
• Multiple deliverables are missing entirely, or are present only as placeholders
• Some genuine engagement with the brief's structure is visible despite the gaps
• The submission shows partial assembly of a portfolio rather than a complete one		• At least seven of the twelve required components are complete
• Two or three components are missing key required elements (e.g., no two-model comparison, no time-savings basis, no retargeting note)
• The bulk of the required structure is present despite these gaps		• At least ten of the twelve required components are fully present and complete
• Any remaining components are present but missing a minor element (e.g., the automation audit is thin, or one C.R.E.A.T.E. label is missing from the log)
• All required content needed for grading is inside the PDF itself
• The portfolio is complete enough that no deliverable is genuinely missing		• All nine Section 1 deliverables are present and fully complete: audience/goal frame, four-part LinkedIn profile mockup with the two-model comparison, manager statement, productivity pitch with estimates and automation audit, reflection image with prompt, reflection post, discussion post, retargeting note, and personal AI line
• All three Section 2 components are present and complete: prompt & iteration log, process documentation, and final reflection
• Nothing required for grading has been left out of the PDF and pushed into an ungraded link
• The portfolio reads as one coherent, fully assembled document rather than a set of loosely stapled parts
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2. Name: Audience-Targeted Drafting (C.R.E.A.T.E. Application)
Description: Learner applies the C.R.E.A.T.E. framework to draft content correctly targeted to each stated audience: the LinkedIn profile (headline, about, experience, skills) for the audience named in the Audience & Goal Frame; the reflective statement for a manager; the reflection post for a professional network; and the discussion post for course peers.
Source: Generative AI: Fundamentals to Future-Ready — MAS Brief: Tasks 1, 2, 3, 6, 7 (Audience & Goal Frame; LinkedIn Profile Rewrite; Reflective Statement to Manager; LinkedIn Reflection Post; Reflective Discussion Post). Evolved from v1 criteria 'Effectiveness' and 'Clarity and Accuracy'.		The audience-targeted drafts are missing entirely, or show no meaningful attempt to address a stated audience.• The audience/goal frame is missing or too vague to guide anything downstream
• The LinkedIn profile is incomplete or largely generic across its sections
• The manager, network, and peer drafts read as near-duplicates of each other
• Some relevant content is present, showing an attempt at each task, but audience targeting is not evident		• The audience/goal frame is present but vague (e.g., 'recruiters' with no further specificity)
• The LinkedIn profile is complete but generic in places
• At least one of the manager/network/peer drafts reads similarly to another, showing limited audience differentiation
• Some C.R.E.A.T.E. elements are visible despite the lack of specificity		• The audience/goal frame is specific enough to guide drafting
• The LinkedIn profile sections are complete and professional, with only minor generic phrasing
• The manager statement, reflection post, and discussion post are each recognizably suited to their audience
• Most C.R.E.A.T.E. elements are evident across the drafts, even if not perfectly executed		• The audience/goal frame names a specific, non-generic target and every subsequent draft is visibly written for it
• All four sections of the LinkedIn profile are specific, achievement-oriented, and attract the named target
• The manager statement, reflection post, and discussion post are each written in a tone and structure genuinely suited to that audience
• C.R.E.A.T.E. elements (context, relevance, execution, audience, tone, examples) are all identifiable in how the drafts differ from each other
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3. Name: Retargeting Analysis and Model Comparison
Description: Learner explains, in the Retargeting Note, what changed and why across the three audience-specific drafts (manager, network, peers), naming which C.R.E.A.T.E. elements shifted and which stayed fixed. Separately, in the LinkedIn Profile task, the learner runs one section through two different LLM tools and names the specific difference that decided which output was kept.
Source: Generative AI: Fundamentals to Future-Ready — MAS Brief: Tasks 2 (two-model comparison) and 8 (Retargeting Note). Evolved from v1 criterion 'Critical Thinking and Problem Solving'.		No retargeting note or model comparison is submitted, or what is submitted contains no actual comparison or analysis.• The retargeting note or model comparison is attempted but says little that would let a reader judge what actually changed or why
• One of the two required comparisons (retargeting note, two-model comparison) is missing or is a restatement rather than an analysis
• Some relevant language is present, showing partial engagement with the requirement		• The retargeting note describes differences at a surface level without clearly tying them to C.R.E.A.T.E. elements
• The two-model comparison is present but the deciding factor is vague or generic (e.g., 'it sounded better')
• Some genuine comparison is attempted, but the reasoning is thin		• The retargeting note identifies real differences between the three drafts and gives a reasonable explanation for most of them
• At least some C.R.E.A.T.E. elements are correctly named as having shifted or stayed fixed
• The two-model comparison includes both outputs and a defensible reason for the choice
• The analysis is grounded in the learner's actual drafts rather than generic statements about audience		• The retargeting note precisely names what changed between the manager, network, and peer drafts and explains why, tying each change to specific C.R.E.A.T.E. elements
• The note correctly identifies which C.R.E.A.T.E. elements stayed fixed across all three, not just what changed
• The two-model comparison pastes both real outputs, names a specific deciding difference (accuracy, tone, structure, invention), and states which tool the learner would reach for next and for what
• If all three drafts ended up reading the same, the note reports and diagnoses that honestly rather than overstating differentiation
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4. Name: AI Productivity Pitch: Practical Judgment
Description: Learner pitches four to eight concrete generative-AI use cases across brainstorming, drafting, pitching, and summarizing, each drawn from real work the learner does; estimates the time saved for each with a stated, defensible basis; and audits the weakest example on the list, correctly judging whether it actually needs a generative model or is really a simpler automation problem (rule, template, filter, macro).
Source: Generative AI: Fundamentals to Future-Ready — MAS Brief: Task 4 (AI Productivity Pitch, including the automation audit). No direct v1 counterpart — this task did not exist in the SNHU-derived v1 rubric.		No productivity pitch is submitted, or what is submitted contains no real examples, estimates, or automation judgment.• Fewer than four examples are given, or most are generic rather than drawn from the learner's real work
• Time estimates are largely unexplained or missing
• The automation audit is missing, or restates the example without judging whether it needs a generative model
• Some relevant content is present, showing partial engagement with the requirement		• Four to five examples are given but coverage across the four categories is uneven
• Some time estimates are given without a stated basis
• The automation audit is attempted but the reasoning for the classification is weak or generic		• At least six examples are given, reasonably distributed across the four categories
• Most time-savings estimates state a basis, even if a couple are thinner than others
• The automation audit picks a real candidate and gives a plausible reason for its classification
• The pitch as a whole is grounded in the learner's actual work rather than generic AI use cases		• Four to eight concrete examples are given across all four categories (brainstorming, drafting, pitching, summarizing), each drawn from real, specific work the learner does
• Every time-savings estimate states its basis (how long the task takes now, and how that is known) rather than an unexplained number
• The automation audit correctly identifies the weakest example and gives a specific, sound reason why it is (or is not) really an automation problem rather than a generative-AI use case
• The distinction between generative AI and simpler automation is drawn with genuine technical judgment, not just asserted
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5. Name: Prompt Engineering and Iteration Practice
Description: Learner's Prompt & Iteration Log shows C.R.E.A.T.E. elements labeled in key prompts and includes at least one full iteration chain (first attempt, what was wrong with it, what was changed, and the resulting output) rather than only final, polished prompts.
Source: Generative AI: Fundamentals to Future-Ready — MAS Brief: Section 2, Prompt & Iteration Log. Evolved from v1 criterion 'Effectiveness' (which addressed iteration only in general terms).		No prompt and iteration log is submitted, or what is submitted shows no evidence of actual prompting or iteration.• The log contains only final prompts with no visible iteration or diagnosis
• C.R.E.A.T.E. labeling is largely absent
• Some relevant content is present, showing an attempt to document prompting, but no real iteration chain is demonstrated		• Some prompts are logged but C.R.E.A.T.E. labeling is inconsistent or missing in places
• An iteration chain is implied but not clearly shown step by step
• The log demonstrates some genuine prompting activity despite these gaps		• Most key prompts have C.R.E.A.T.E. elements labeled
• At least one iteration chain is shown, though it may be less detailed than an exemplary log
• The log gives a reader enough detail to see how prompting evolved across at least one deliverable		• C.R.E.A.T.E. elements are explicitly labeled in the key prompts behind the profile and the three reflections
• At least one full iteration chain is shown: the first attempt, a clear diagnosis of what was wrong with it, the change made, and the resulting output
• The log covers prompting behind more than one deliverable, not just a single task
• The log demonstrates genuine back-and-forth refinement, not prompts written once and never revisited
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6. Name: Reflection, Voice, and Professional Integrity
Description: Learner's Personal AI Line honestly accounts for anything AI overstated about them and what was corrected, states a real position on disclosure appropriate to each audience, and sets out standing rules for future professional AI use with reasoning; the reflection image and its prompt/rationale, and the final reflection, are specific to the learner's own experience rather than generic.
Source: Generative AI: Fundamentals to Future-Ready — MAS Brief: Tasks 5 (Reflection Image) and 9 (Personal AI Line); Section 2 Process Documentation and Final Reflection. Evolved from v1 criterion 'Creativity and Innovation' (image handling) and 'Critical Thinking and Problem Solving' (ethics framing).		No personal AI line, reflection image, or final reflection is submitted, or what is submitted contains no meaningful content.• The personal AI line is largely generic AI-ethics language rather than an account of this learner's own portfolio
• The reflection image or its rationale is missing or disconnected from the reflection content
• The final reflection reads as boilerplate with little specific to this learner's experience
• Some relevant language is present, showing partial engagement with the requirement		• The personal AI line touches on truthfulness, disclosure, or rules, but treats them at a surface level
• The reflection image or rationale is present but only loosely tied to what was learned
• The final reflection is generic in places but includes some genuine content		• The personal AI line addresses truthfulness, disclosure, and standing rules, with reasonable specificity
• The disclosure position is stated even if not fully differentiated by audience
• The reflection image and rationale are clearly connected to the course experience
• The final reflection identifies real takeaways specific to this portfolio		• The personal AI line honestly identifies anything AI drafted that overstated the learner's record, or explains specifically how it was checked if nothing needed correcting
• It states a clear, audience-differentiated disclosure position (recruiter vs. manager vs. network) rather than a single blanket answer
• It sets out two or three standing rules with genuine reasoning behind each, written in the learner's own voice
• The reflection image's prompt and rationale are specific to what the learner actually learned, and the final reflection is concrete and self-aware rather than generic
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