Introduction: The Feature Trap
When IT directors evaluate platforms, they read marketing materials full of capability lists.
Screen sharing. Breakout rooms. Polls. Reactions. Virtual whiteboards. Live chat. Hand raising. Q&A panels. Recording with transcripts. Automatic captions. Integration with 20+ LMS platforms. AI-powered meeting summaries. Custom backgrounds. Raise hand with rankings. Participant reactions with emoji.
It’s dizzying. More features always seems better. It seems to offer flexibility. It seems to cover every use case.
Until an institution adopts a feature-rich platform and discovers: faculty are overwhelmed by options. Students miss key interaction because they’re navigating menus. Support tickets increase because there are more things to learn wrong. The system is powerful but unpredictable.
This article is about a decision that leadership committees rarely make explicitly, but should: Prioritize stability over features.
What Stability Means in an Academic Context
Stability isn’t exciting. It’s boring. That’s the point.
Same experience every week. Faculty teach on Monday with a certain experience. That experience should be identical on Friday, in Week 3, and in Week 15. No surprise changes. No features turning on or off. No automatic updates that change the interface mid-semester. Predictability is the foundation.
No surprises during exams. Exam weeks are high-pressure. The system should work exactly as it always has. No new versions rolling out. No experimental features. No “improvements” that change session behavior. During exams, stability is non-negotiable.
Predictable operations. When a class is scheduled, it starts reliably. When students join, the join process is the same every time. When recording is enabled, the recording saves to the same location, consistently. When a feature is used, it behaves the same way repeatedly. Consistency builds confidence.
Low support burden. The system rarely needs troubleshooting. When it does, the problem is clear and reproducible. Faculty encounter the same behaviors, so support teams recognize patterns. Help is fast because the system is simple enough that everyone understands it.
How Feature Overload Creates Risk
More features sound like more capability. In practice, they’re often more risk.
Complexity. Each feature adds complexity. Complexity creates failure modes. A system with 10 features has 10 possible problems. A system with 50 features has 50+ possible problems, because features interact. Problems interact. Debugging becomes harder. Failure becomes less predictable.
Faculty confusion. When there are many features, faculty don’t use them consistently. Some instructors use polls, some don’t. Some enable chat, some disable it. Some use breakout rooms, some don’t. Within a single institution, 50 versions of “how to teach online” emerge. Students experience inconsistency. They get confused about what they’re supposed to do in each class.
Support burden. With many features comes many questions. Faculty ask: “Should I use polls or reactions?” “Can I use breakout rooms with 200 students?” “Why aren’t captions working?” Support teams are flooded with feature-use questions instead of reliability problems. Burnout increases.
Update fatigue. Platforms with many features update frequently. New features are added. Old features are redesigned. Updates happen monthly, weekly, or continuously. Faculty relearn interfaces. New edge cases appear. The platform they taught successfully on last month behaves differently this month. Stability erodes.
Over-reliance on features to solve pedagogy. A feature-rich platform tempts institutions to think: “Let’s use polls for engagement!” “Let’s use breakout rooms for collaboration!” But good pedagogy comes from design, not features. A simple system with clear pedagogy outperforms a feature-rich system with unclear purpose.
Why Institutions Prefer Boring but Reliable Systems
Leadership committees rarely articulate this, but they sense it: boring systems are safer bets.
A system that does one thing reliably is more valuable than a system that does fifty things inconsistently. A system that hasn’t changed in three years is more predictable than a system updated constantly. A system with five clicks to join is better than a system where the join experience depends on which features are enabled.
This isn’t conservative bias. It’s risk awareness.
An institution betting its academic continuity on a platform is betting that the platform will remain stable through changes in leadership, changes in faculty, changes in student cohorts, and changes in regulatory environment. A feature-rich, constantly-evolving platform creates uncertainty. A stable, simple platform creates confidence.
Stability as an Approval Criterion
When institutional decision-makers evaluate platforms, they should be asking:
- Has this platform’s core behavior changed in the past three years? If yes, flag it. If the platform has redesigned its user experience, changed its architecture, or overhauled features, it’s not stable.
- How often are updates deployed? If updates happen continuously, the platform is in active development. That means change. Change means risk.
- Can features be disabled system-wide? If the institution doesn’t want video recording available, can IT enforce that? Or will the feature exist, and IT has to train everyone to ignore it? Control is a proxy for stability.
- Do customers report consistent behavior over years? Talk to institutions that have used the platform for five years. Do they say, “It just works the same every year”? Or do they say, “We have to relearn it every update”?
How to Evaluate Stability Without Deep Technical Analysis
Usage patterns. Ask: What do most customers actually use? If the platform has 50 features but 80% of usage is in five of them, the platform is effectively simpler than its feature list suggests.
Pilot behavior. In a pilot with faculty, did problems get better or worse over time? If stability increased, the platform is learning your usage. If new issues emerged as the pilot progressed, the system creates problems at scale.
Faculty feedback. Directly ask: Did this feel predictable? Would you recommend this to peers? Would you teach on this again? If faculty express doubt, that’s a stability signal. If they express confidence, that’s significant.
Support ticket trends. What questions did IT receive? Were they mostly feature-use questions, or reliability questions? If 80% of tickets are “How do I use feature X?” that’s a complex platform. If 80% are “I can’t join,” that’s a stability issue.
Conclusion
Stability earns trust. Features don’t.
An institution commits to a platform for years. It trains faculty on it. It builds policies around it. It becomes part of the academic calendar. That commitment only makes sense if the platform is reliable, predictable, and unlikely to create surprises.
Feature-rich platforms create surprises. They change. They evolve. They require continuous learning. They fragment adoption across your institution as different faculty use different features.
Stable platforms are boring. They work the same way every week. Faculty learn them once. Students know what to expect. It just works.
When evaluating platforms, ask not “what can it do?” but “will it be reliable every week for the next five years?” The answer to the second question matters more.