UI & UX
AuraScore 79/100

Academic Repository Discovery Friction Matrix

Evaluate discovery, metadata filtering, and citation retrieval interactions across scholarly research repositories.

Use this template when auditing or redesigning digital research repositories, institutional archives, or preprint servers. It guides UX researchers in identifying interaction bottlenecks and scoring remediation priorities across academic search journeys.

Template

Role: Principal UX Researcher and Information Architect specializing in scholarly digital libraries.

Context

  • Institution context: {{institution_type}}
  • Primary user cohort: {{target_researcher_tier}}
  • Corpus and archive scale: {{repository_scope}}
  • Documented user friction: {{current_search_pain_points}}
  • Schema and citation protocols: {{metadata_standards}}
  • Core researcher milestones: {{primary_user_goals}}

Task

Synthesize search, metadata filtering, and citation retrieval friction points across the repository discovery experience into an actionable UX evaluation and remediation matrix to optimize academic retrieval efficiency.

Method

  1. Map the end-to-end information retrieval journey from initial query reformulation to full-text citation export based on {{repository_scope}}.
  2. Analyze cognitive load patterns across faceted filtering controls given {{current_search_pain_points}} and {{target_researcher_tier}} expectations.
  3. Audit metadata visualization clarity, provenance indicators, and abstract parsing according to {{metadata_standards}}.
  4. Evaluate citation export and dataset access pathways against documented benchmark speeds and usability heuristics.
  5. Score each identified interaction barrier on task impact, frequency, cognitive friction, and engineering effort.
  6. Formulate high-confidence interaction design counter-proposals for every high-friction touchpoint.
  7. Structure findings into a two-dimensional prioritization matrix categorizing findings by user friction level and implementation feasibility.

Constraints

  • Recommendations MUST adhere to universal search heuristics and comply with {{metadata_standards}} conventions.
  • You MUST NOT recommend proprietary dark patterns or forced sign-in gates before citation previews.
  • Every identified friction point MUST map directly to one or more of {{primary_user_goals}}.
  • Keep interaction terminology precise (e.g., progressive disclosure, facet pagination, syntax parsing).

Output format

  1. Executive Synthesis (150-200 words summarizing key information architecture bottlenecks).
  2. Journey Phase Diagnostic Table (Phase, User Action, Observed Friction, Heuristic Violated).
  3. Repository UX Prioritization Matrix (Markdown table with columns: Interaction Touchpoint, Friction Severity [High/Med/Low], Feasibility [High/Med/Low], UX Remediation, Expected Impact on {{target_researcher_tier}}).
  4. Technical & Schema Governance Notes (bulleted list of 4-6 implementation dependencies).

Self-review

  • Did I directly incorporate all {{current_search_pain_points}} into the evaluation?
  • Does the matrix clearly separate friction severity from technical feasibility?
  • Are citation, metadata, and licensing requirements accurately accounted for?
AuraScore breakdown
79/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering10/12 · Adequate

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

design-visual
design-ui-ux
education-research
information-architecture
academic-ux
discovery-search