WGU Reddit Feedback Analyzer

You are a course designer reviewing a Reddit post about a WGU course.

A "pain point" is a negative student experience clearly caused by how the course is designed, delivered, or supported
(materials, assessments, grading, instructions, pacing, support, etc.).

A "root cause" is the specific fixable course
issue that produces that pain point.

Read the post and decide whether it contains one or more course-related pain points. If there are several, list each
distinct pain point. Merge complaints that share the same root cause into one pain point.

Return exactly one JSON object with this schema:

{
"contains_painpoint": "y" | "n" | "u",
"confidence": number between 0.0 and 1.0,
"pain_points": [
{
"root_cause_summary": "...",
"pain_point_snippet": "..."
}
]
}

Rules:
	•	"contains_painpoint":
	•	"y": clearly at least one course-related pain point.
	•	"n": clearly no course-related pain point.
	•	"u": too vague or mixed to decide.
	•	If "contains_painpoint" is "y":
	•	For each item in "pain_points":
	•	"root_cause_summary": a few words from the designer's perspective (e.g., "practice test misaligned with final", "unresponsive instructors").
	•	"pain_point_snippet": one short direct quote from the post that shows that pain point.
	•	If "contains_painpoint" is "n" or "u":
	•	Set "pain_points" to [].
	•	"confidence": your confidence in the chosen label, where 1.0 = very certain and 0.0 = very uncertain.

Do NOT include any extra fields or wrap the JSON in Markdown.

Reddit post metadata:
	•	post_id: {post_id}
	•	course_code: {course_code}

Reddit post text:
{post_text}

You will receive social media posts about university course {course_code} - {course_title}.

Your task is to cluster the posts by their root-cause.

A pain point is a specific, actionable issue in the course's design, delivery, instructions, materials, or support-a friction that WGU can realistically fix or improve.
Do not group based on general frustration, motivation, or personal struggle.
Cluster only the actionable, course-side issues described in the summaries/snippets.

Your responsibilities:
	1.	Cluster posts by shared root-cause issue, from the perspective of WGU course designers.
	2.	Focus only on actionable pain points (unclear instructions, outdated materials, rubric mismatches, broken tools, missing resources, etc.).
	3.	Produce clean JSON only, strictly following the structure below.
	4.	Assign cluster_ids using the format: COURSECODE_INT (example: C211_1).
	5.	Sort clusters by largest size first.
	6.	A post may appear in multiple clusters if appropriate.
	7.	Output raw JSON only - no explanations, no commentary.

---

REQUIRED JSON STRUCTURE

{
  "courses": [
    {
      "course_code": "COURSE_CODE",
      "course_title": "COURSE_TITLE",
      "total_posts": 0,
      "clusters": [
        {
          "cluster_id": "COURSECODE_1",
          "issue_summary": "short root-cause description",
          "num_posts": 0,
          "post_ids": [
            "post_id_1",
            "post_id_2"
          ]
        }
      ]
    }
  ]
}


---

EXAMPLE OUTPUT

{
  "courses": [
    {
      "course_code": "C211",
      "course_title": "Scripting and Programming - Applications",
      "total_posts": 5,
      "clusters": [
        {
          "cluster_id": "C211_1",
          "issue_summary": "no instructor response for approvals or passwords",
          "num_posts": 3,
          "post_ids": ["1ci7efm", "1e12ncu", "1i5z4ev"]
        },
        {
          "cluster_id": "C211_2",
          "issue_summary": "OA result stuck, blocking progress",
          "num_posts": 1,
          "post_ids": ["1lu24vq"]
        },
        {
          "cluster_id": "C211_3",
          "issue_summary": "PA doesn't match OAs",
          "num_posts": 1,
          "post_ids": ["1m2hl4r"]
        }
      ]
    }
  ]
}

You will receive a flat list of Stage-2 course-level pain point clusters.

Each cluster has:
	•	cluster_id
	•	issue_summary
	•	course_code (input only - do NOT include in output)
	•	course_title (input only - do NOT include in output)
	•	num_posts

Your job is to merge these into cross-course global issues.

---

ROOT-CAUSE CLASSIFICATION RULES
	•	Group clusters by the fixable course-side root cause only.
	•	If a summary includes both a cause and a symptom (e.g., "wrong resource list causing confusion"), classify by the course-side cause, never by the student symptom.
	•	Student feelings (confusion, stress, frustration, delay) are not categories.
	•	Exclusive assignment: each cluster_id appears once in the final output.

---

USE SEEDED GLOBAL ISSUE TYPES WHEN THEY MATCH

Reuse these labels exactly when applicable:

unclear_or_ambiguous_instructions
assessment_material_misalignment
platform_or_environment_failures
missing_or_low_quality_materials
evaluator_inconsistency_or_poor_feedback
workflow_or_policy_barriers
missing_or_broken_resources
instructor_or_support_unresponsiveness
tooling_environment_misconfiguration_or_guidance
grading_or_answer_key_or_process_issues
workload_or_scope_issues
proctoring_or_exam_platform_issues
scheduling_or_simulation_availability_issues
ai_detection_confusion_and_false_flags
prerequisite_gaps_or_unpreparedness
external_tool_dependency_risks

If a cluster fits one of these meanings, reuse that issue.
Create a new global issue only if no seeded one matches.

---

SORTING RULE

Sort global_clusters by total num_posts per issue (sum across its member clusters), highest first.

---

OUTPUT FORMAT (STRICT)

{
  "global_clusters": [
    {
      "course_issue_description": "string",
      "normalized_issue_label": "string",
      "member_cluster_ids": ["id1","id2"]
    }
  ],
  "unassigned_clusters": ["cluster_id_a"]
}

	•	No text outside JSON
	•	Do not include course_code or course_title
	•	Every cluster_id must appear exactly once