Agent StoreHuman ResourcesFaculty Workload and Teaching Assignment Management
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Instructor Workload Balancing Agent

Human ResourcesFaculty Workload and Teaching Assignment Management

Analyzes teaching loads, advising duties, and committee assignments to rebalance instructor workload fairly across a department each term.

4
Process steps
5
Integrations
3
Data inputs

Department chairs are responsible for distributing teaching sections, academic advising caseloads, and committee service fairly across faculty, but this is typically done from memory and spreadsheets updated inconsistently, which quietly lets some instructors accumulate an outsized load while others are underutilized

Overloaded instructors burn out and their course quality suffers, while the imbalance often isn't visible until a formal grievance or an accreditation workload review forces the issue

This agent pulls every component of instructor workload, including teaching credit hours, advising caseload, committee service, and research release time, into a single normalized workload score per faculty member and flags significant imbalances before they become a retention or quality risk

It then proposes specific reassignment options, such as which course sections or advisees could shift to underloaded instructors, ranked by minimal disruption

The agent aggregates teaching assignments, advising caseloads, committee memberships, and any approved release time from the HR and academic scheduling systems, then converts each component into a standardized workload-credit value using the department's approved workload policy formula. It calculates a total workload score per instructor and compares it against the department's target range and each faculty member's contractual load expectations, flagging anyone significantly over or under. For flagged imbalances, it generates ranked rebalancing proposals, such as moving a specific course section or a subset of advisees to an underloaded instructor with matching qualifications, and estimates the resulting workload scores after each proposed change.

1

Aggregate Workload Components

  • Pull teaching assignments and credit hours
  • Pull advising caseloads and committee memberships
  • Pull approved release time and research load
Outcome: Every component of each instructor's workload is consolidated into one record.
2

Calculate Normalized Scores

  • Apply the department's workload-credit formula
  • Compute total workload score per instructor
  • Compare against contractual and target load ranges
Outcome: Every faculty member has a single comparable workload score.
3

Detect Imbalances

  • Flag instructors significantly over or under target load
  • Rank imbalance severity by department and program
  • Identify qualification matches for potential reassignment
Outcome: Workload imbalances are surfaced with clear severity ranking.
4

Propose Rebalancing Options

  • Generate ranked reassignment proposals for flagged cases
  • Estimate resulting workload scores post-change
  • Present proposals to the chair for approval
Outcome: Chairs receive specific, data-backed options to equitably rebalance workload.
Workday HCM
Ellucian Banner
Interfolio
Microsoft Excel
Kronos