Assignment: Create and label a diagram showing the four-step image recognition process
Total Points: 20
| Criteria | Exemplary (5 pts) | Proficient (4 pts) | Developing (3 pts) | Beginning (2 pts) | Not Yet (0-1 pt) |
|---|---|---|---|---|---|
| Accuracy of Steps | All four steps correctly identified and sequenced: Input → Feature Extraction → Pattern Matching → Classification | All four steps present with minor inaccuracies in details | 3 of 4 steps correct or sequence has errors | Only 2 steps correct or significant conceptual errors | Missing steps or fundamentally incorrect process |
| Visual Clarity | Diagram is exceptionally clear with logical flow, arrows, and visual organization; easy to follow | Diagram is clear and organized with adequate visual flow | Diagram is somewhat organized but could be clearer; flow is present but confusing | Diagram is disorganized; flow is unclear or absent | No clear diagram structure; incomprehensible layout |
| Labels and Descriptions | All components labeled with detailed, accurate descriptions explaining what happens at each step | All components labeled with adequate descriptions | Most components labeled but descriptions lack detail or have minor errors | Few labels; descriptions are vague or incorrect | Missing labels or descriptions are mostly incorrect |
| Vocabulary Use | Correctly uses 5+ key terms: pixels, RGB, training data, features, patterns, confidence score, classification | Correctly uses 4 key terms with appropriate context | Uses 2-3 key terms, some may be used incorrectly | Uses 1-2 key terms or uses them incorrectly | No technical vocabulary or all terms used incorrectly |
Comments:
Total Score: ______ / 20
Assignment: Write 1-2 paragraphs explaining how AI learns to recognize images using training data, with at least one specific example
Total Points: 20
| Criteria | Exemplary (5 pts) | Proficient (4 pts) | Developing (3 pts) | Beginning (2 pts) | Not Yet (0-1 pt) |
|---|---|---|---|---|---|
| Understanding of Training Data | Clearly explains that AI learns from thousands of labeled examples and why diversity matters; shows deep understanding | Explains AI learns from examples; mentions importance of quantity/quality | Mentions training data but explanation is superficial or partially incorrect | Vague reference to AI learning; doesn't clearly explain training data | No understanding of training data concept evident |
| Specific Example | Provides detailed, relevant example with clear explanation of how training works (e.g., "showing AI 10,000 dog photos of different breeds...") | Provides adequate example that illustrates concept | Provides example but it's generic or doesn't clearly illustrate concept | Attempts example but it's incorrect or irrelevant | No example provided |
| Writing Quality | Well-organized paragraphs with topic sentences, clear transitions, and strong conclusion; 1-2 paragraphs as requested | Organized writing with clear main ideas and adequate development | Basic organization; ideas are present but development is weak | Poorly organized; ideas are disconnected or unclear | No clear organization; writing is incomprehensible |
| Technical Accuracy | All statements are scientifically accurate; no misconceptions | Mostly accurate with only minor errors | Some accurate information but contains misconceptions | Significant misconceptions or inaccuracies | Fundamentally incorrect understanding |
Comments:
Total Score: ______ / 20
Assignment: Create a comparison showing at least three differences between human vision and computer vision
Total Points: 15
| Criteria | Exemplary (5 pts) | Proficient (4 pts) | Developing (3 pts) | Beginning (2 pts) | Not Yet (0-1 pt) |
|---|---|---|---|---|---|
| Number of Differences | Identifies 5+ meaningful differences with specific details | Identifies 4 meaningful differences | Identifies 3 differences | Identifies only 1-2 differences | Identifies no clear differences or all are incorrect |
| Depth of Analysis | Each difference includes explanation of why/how with specific examples; shows sophisticated understanding | Each difference explained clearly with some detail or examples | Differences listed but explanations are basic or missing detail | Differences listed with little to no explanation | No meaningful analysis; just words without understanding |
| Accuracy | All comparisons are accurate and demonstrate clear understanding of both systems | Mostly accurate; minor errors don't affect overall understanding | Some inaccuracies but core understanding is present | Several significant errors or misconceptions | Mostly or entirely inaccurate |
Comments:
Total Score: ______ / 15
Assignment: Identify one use of image recognition in your life and explain one benefit and one concern
Total Points: 15
| Criteria | Exemplary (5 pts) | Proficient (4 pts) | Developing (3 pts) | Beginning (2 pts) | Not Yet (0-1 pt) |
|---|---|---|---|---|---|
| Application Identification | Identifies specific, relevant real-world application with details about how it works | Identifies clear real-world application with adequate description | Identifies application but description is vague or generic | Application mentioned is questionable or very generic | No clear application identified or entirely irrelevant |
| Benefit Explained | Benefit is clearly explained with specific reasons why it's helpful; shows understanding of impact | Benefit identified and explained with adequate reasoning | Benefit mentioned but explanation is superficial | Benefit stated but not explained or explanation doesn't make sense | No benefit identified or completely incorrect |
| Concern Explained | Concern is thoughtful and specific; may include privacy, bias, accuracy, or ethical considerations; well-reasoned | Valid concern identified with reasonable explanation | Concern mentioned but explanation is basic or unclear | Concern is vague or seems unrelated to the technology | No concern identified or completely irrelevant |
Comments:
Total Score: ______ / 15
Assignment: Successfully train an image classification model and document process/results
Total Points: 20
| Criteria | Exemplary (5 pts) | Proficient (4 pts) | Developing (3 pts) | Beginning (2 pts) | Not Yet (0-1 pt) |
|---|---|---|---|---|---|
| Model Training | Successfully trained model with 30+ diverse images per class; model performs well | Trained model with adequate images (20-30); model works reasonably well | Trained model but limited images (<20) or limited diversity; model has inconsistent performance | Attempted training but model doesn't work well due to insufficient or poor-quality images | Did not successfully train a model |
| Testing and Observation | Extensively tested model with various scenarios; documented what works well and what causes errors | Tested model adequately; documented basic observations | Limited testing; minimal documentation | Very limited testing; little to no documentation | No testing or documentation |
| Reflection Quality | Insightful reflections on why AI succeeded/failed; connects to lesson concepts; identifies patterns | Good reflections with clear observations about AI behavior | Basic reflections; some observations but lack depth | Minimal reflection; observations are superficial | No meaningful reflection |
| Worksheet Completion | Worksheet fully completed with thoughtful, detailed responses | Worksheet completed with adequate responses | Worksheet partially completed or responses lack detail | Worksheet minimally completed; most responses incomplete | Worksheet not completed or responses are blank/invalid |
Comments:
Total Score: ______ / 20
Use throughout lesson; teacher observation notes
Total Points: 10
| Criteria | Exemplary (3-4 pts) | Proficient (2 pts) | Developing (1 pt) | Not Yet (0 pts) |
|---|---|---|---|---|
| Engagement | Actively engaged throughout; asks questions; makes connections; shows curiosity | Generally engaged; participates when asked; follows along | Sometimes engaged; frequently off-task or passive | Rarely engaged; consistently off-task |
| Collaboration | Works exceptionally well with partner/group; contributes ideas; listens to others; helps peers | Works well with others; shares tasks fairly; communicates adequately | Struggles with collaboration; may dominate or disengage from group | Does not collaborate effectively; conflicts or complete disengagement |
| Use of Class Time | Uses time efficiently; stays on task; completes activities; helps others when finished early | Uses time adequately; completes most activities with appropriate effort | Uses time poorly; off-task frequently; rushes through activities | Wastes time; does not complete activities; distracts others |
Observation Notes:
Total Score: ______ / 10
Assignment: Create video explanation, AI system proposal, or other extension project
Total Points: 30 (Extra Credit or Alternative Summative)
| Criteria | Exemplary (6 pts) | Proficient (5 pts) | Developing (3-4 pts) | Beginning (1-2 pts) | Not Yet (0 pts) |
|---|---|---|---|---|---|
| Content Accuracy | All information is accurate, detailed, and demonstrates deep understanding | Content is mostly accurate with good understanding demonstrated | Content has some inaccuracies or shows partial understanding | Content has significant errors or misconceptions | Content is mostly incorrect or incomplete |
| Creativity/Originality | Highly creative approach; original thinking; goes beyond lesson content | Shows creativity; fresh ideas or perspectives | Some creative elements but mostly follows standard approaches | Little creativity; generic or copied approach | No creativity; minimal effort evident |
| Organization | Exceptionally well-organized and easy to follow; logical flow; professional quality | Well-organized with clear structure and adequate flow | Basic organization; structure is present but could be improved | Poorly organized; hard to follow; lacks structure | No clear organization; incomprehensible |
| Technical Execution | High-quality production; appropriate use of tools/media; polished final product | Good quality; adequate use of tools; complete final product | Adequate quality but technical issues or incomplete elements | Poor quality; significant technical problems | Very low quality or unfinished |
| Depth of Thinking | Shows sophisticated analysis, synthesis, or problem-solving; addresses complex questions | Shows good thinking skills; makes connections; adequate depth | Shows basic thinking; surface-level analysis | Shows limited thinking; very superficial | Shows no meaningful thinking or analysis |
Comments:
Total Score: ______ / 30
Assignment: Demonstrate understanding of key terms through matching, definition writing, or usage in context
Total Points: 16 (2 points per term)
Terms to Assess: Pixel, Pattern Recognition, Training Data, Classification, Computer Vision, Feature Extraction, Confidence Score, Machine Learning
| Term | Fully Understands (2 pts) | Partially Understands (1 pt) | Does Not Understand (0 pts) |
|---|---|---|---|
| Pixel | Correctly defines as tiny colored square with RGB values; explains role in digital images | Basic definition but missing key details | Incorrect or no definition |
| Pattern Recognition | Explains as process of finding recurring features/arrangements in data; connects to AI | Basic definition but incomplete or vague | Incorrect or no definition |
| Training Data | Explains as set of labeled examples used to teach AI; mentions importance of diversity | Basic definition but missing key concepts | Incorrect or no definition |
| Classification | Explains as assigning category/label based on features; understands it's AI's output | Basic definition but incomplete | Incorrect or no definition |
| Computer Vision | Explains as AI field enabling computers to interpret visual information | Basic definition but vague | Incorrect or no definition |
| Feature Extraction | Explains as identifying important characteristics (edges, colors, shapes) | Basic definition but incomplete | Incorrect or no definition |
| Confidence Score | Explains as percentage showing AI's certainty about prediction | Basic definition but incomplete | Incorrect or no definition |
| Machine Learning | Explains as AI learning patterns from examples vs. following rigid rules | Basic definition but incomplete | Incorrect or no definition |
Comments:
Total Score: ______ / 16
Use for overall assessment of mastery
Total Points: 25
| Level | Description | Points |
|---|---|---|
| Advanced (22-25 pts) | Student demonstrates comprehensive understanding of how AI processes images, including technical details and real-world implications. Can explain concepts clearly to others, connects ideas across lessons, and thinks critically about ethical implications. Shows ability to apply knowledge to new situations. Consistently uses technical vocabulary correctly. | 22-25 |
| Proficient (18-21 pts) | Student demonstrates solid understanding of core concepts. Can explain the image recognition process, role of training data, and differences between human and computer vision. Understands real-world applications and basic ethical concerns. Uses vocabulary correctly most of the time. May need support with more complex applications or connections. | 18-21 |
| Developing (14-17 pts) | Student demonstrates partial understanding of concepts. Grasps basic ideas about pixels and how AI processes images but may have gaps in understanding training data importance or real-world applications. Uses some vocabulary correctly but inconsistently. Needs support to explain concepts clearly or make connections. | 14-17 |
| Beginning (10-13 pts) | Student demonstrates limited understanding. Recognizes that AI processes images differently than humans but cannot explain details accurately. Struggles with vocabulary and has difficulty making connections. May confuse key concepts. Needs significant support and reteaching. | 10-13 |
| Not Yet (0-9 pts) | Student shows minimal or no understanding of core concepts. Cannot explain how AI processes images or why training data matters. Does not use vocabulary correctly. Needs intensive intervention and may require alternative instruction approaches. | 0-9 |
Supporting Evidence:
Total Score: ______ / 25
For students to evaluate their own learning
Name: Date:
| Statement | Strongly Agree (4) | Agree (3) | Disagree (2) | Strongly Disagree (1) |
|---|---|---|---|---|
| I understand how AI processes images as pixel data | □ | □ | □ | □ |
| I can explain what training data is and why it matters | □ | □ | □ | □ |
| I can describe the four steps in image recognition | □ | □ | □ | □ |
| I understand differences between human and computer vision | □ | □ | □ | □ |
| I can explain at least one real-world use of image recognition | □ | □ | □ | □ |
| I understand ethical concerns about facial recognition | □ | □ | □ | □ |
| I can use vocabulary like pixel, classification, and training data correctly | □ | □ | □ | □ |
| I worked well with my partner/group during activities | □ | □ | □ | □ |
| I asked questions when I was confused | □ | □ | □ | □ |
| I am interested in learning more about computer vision | □ | □ | □ | □ |
Total Points: ______ / 40
What was easiest for you in this lesson?
What was most challenging?
What would help you understand better?
How can you use what you learned?
If using all rubrics, calculate total possible points and student score:
Total Possible: 20 (Diagram) + 20 (Written) + 15 (Comparison) + 15 (Application) + 20 (Teachable Machine) + 10 (Participation) + 16 (Vocabulary) = 116 points
Grading Scale:
Option 1: Select 3-4 Rubrics
Choose the rubrics most aligned with your learning objectives and grade only those.
Option 2: Weight Rubrics Differently
Assign different weights based on importance (e.g., Diagram = 30%, Teachable Machine = 30%, Written = 20%, Application = 20%)
Option 3: Portfolio Assessment
Collect all work in a portfolio; assess overall growth and understanding holistically using Rubric 9.
Option 4: Mastery-Based
Students must achieve "Proficient" or higher on core rubrics (Diagram, Written, Teachable Machine) to demonstrate mastery. Reteach and reassess if needed.
"Excellent work! Your [specific element] demonstrates deep understanding of [concept]. I especially appreciate [specific strength]. To challenge yourself further, consider [extension idea]."
"Good job! You clearly understand [concept]. Your [specific element] shows solid thinking. To strengthen your work, try [specific suggestion]."
"You're making progress on understanding [concept]. I can see you understand [strength], but [specific area] needs more work. Let's review [concept] together. Try [specific strategy]."
"I can see you're working on understanding [concept]. Let's work together to strengthen [specific area]. Here's what will help: [specific intervention]. Come see me so we can review [concept]."
Before Lesson:
During Lesson:
During Assessment:
After Assessment:
Evolve AI Institute • Lesson 7: How AI Sees Images
Assessment Rubrics for Summative Evaluation
Remember: Rubrics should support learning, not just measure it. Use them as tools for clear communication of expectations, specific feedback, and growth tracking. Adapt them to your students' needs and your teaching context!