The Supervision Problem No One Trains You For
Ask any early-stage academic how they learned to supervise students, and the answer is almost always: “I didn’t.”
Many early-stage researchers (whether late-stage PhD students, postdocs, or new group leaders) find themselves responsible for supervising others without ever having received any formal leadership training. Supervising students often becomes a matter of trial and error, inherited habits, or simply repeating the way they were once supervised. What starts with good intentions frequently spirals into frustration for both supervisor and student.
Despite good intentions, supervision often devolves into frustration. Supervisors often feel drained by constant interruptions, while students may feel either micromanaged or left to their own devices. Both sides silently wonder why this feels harder than it should.
Part of the struggle lies in the mismatch between expectations. The supervisor was hoping for support that would lighten their own workload. Instead, supervision becomes another cognitive load, a source of constant interruptions and decision fatigue.
She’s a third-year postdoc, balancing the tightrope many early-career researchers know all too well. She’s deep into drafting her first grant application, i.e. a make-or-break proposal that could finally secure her independent funding stream. The clock is ticking; her writing window today is short, and she needs uninterrupted focus to make real progress.
But within the first two hours of the morning, Susan is interrupted three times.
Just as she is getting over the initial struggle of putting some words on a blank page, Ernst, who has just started his PhD, pops into her office. He’s stuck. “I tried running the cell culture prep for the assay, but I think one of the plates is contaminated. Can you take a look?”
Fifteen minutes later, Ernst knocks on the frame. “Quick question. For the assay, shall I use 20 or 25 µg of protein?”
And another 20 minutes in, Ernst’s face appears in the doorway. “I’m really sorry, Susan. I can’t get the pH of the buffer right. Can you help?”
By lunchtime, the time Susan set aside for grant writing has evaporated, her cognitive energy depleted, and her afternoon is packed with meetings, and she’s utterly frustrated. The grant proposal? Not even a single paragraph. The pressure mounts, not because she resents Ernst, but because supervising seems to generate constant low-level fragmentation that erodes her own deep work time.
And here’s the thing: Ernst isn’t doing anything wrong. He’s motivated, eager to learn, and genuinely wants to get things right. However, Susan is trapped in a pattern that many new supervisors find themselves in: a mismatch between their leadership style and the developmental stage of the person they’re supervising.
This is where situational leadership in academia changes everything. It offers a framework to break out of this cycle, especially for early-career researchers who find themselves in leadership roles without any or very little formal training.
Table Of Contents:
- The Supervision Problem No One Trains You For
- Situational Leadership: A Framework for Supervising Real Humans
- The Four Leadership Styles in Action
- Where Supervision Breaks Down
- Why Situational Leadership Creates a Different Path
- Frequently Asked Questions (FAQ’s)
- References
Situational Leadership: A Framework for Supervising Real Humans
Paul Hersey and Kenneth Blanchard’s Situational Leadership® model builds on a simple truth: there is no single “best” leadership style. The right approach depends entirely on two things:
- The task itself, and
- The competence and confidence level of the person performing it.
In other words, the best leadership style isn’t static. It is flexible. Where many supervisors struggle is assuming that one approach (whether high control or full autonomy) should apply across all tasks and students. But both premature delegation and chronic micromanagement create friction, hinder learning, and erode confidence on both sides.
Two core behaviours drive this model:
- Directive behaviour: How much the supervisor structures, instructs, and specifies how a task should be performed.
- Supportive behaviour: How much the supervisor listens, encourages reflection, and provides emotional scaffolding.
By combining these two axes, the model identifies four leadership styles, each of which is appropriate depending on where the student stands in relation to a given task. Let’s walk with Susan and Ernst as they experience each stage and discover what each gets from it.
The Four Leadership Styles in Action
1. Directing: The Enthusiastic Beginner
In his first weeks, Ernst is all motivation and no experience. Susan steps in with full directive leadership, i.e. breaking tasks into specific steps, demonstrating every step of the protocol, and reviewing each setup alongside him.
For example, Susan doesn’t just say, “Run the cell culture for these samples tomorrow.” She sits with Ernst at the bench and walks him through buffer prep, pipetting technique, sample loading, and troubleshooting common issues upfront. At this stage, the goal is precision, not autonomy.
Supportive behaviour exists, too. Susan reassures him when he hesitates, but her primary role is to give structure and reduce ambiguity. Left on his own too soon, Ernst would likely flounder.
- What Susan gets out of it: Fewer preventable mistakes, confidence that early errors won’t derail projects, and peace of mind knowing Ernst is learning safely.
- What Ernst gets out of it: Clarity. He’s not left guessing. The structure builds his initial confidence and allows early success.
What can go wrong when mismatched: Delegating too early leaves Ernst overwhelmed and vulnerable to basic mistakes.
So, without this phase, early mistakes could discourage Ernst before he even builds basic competence.
2. Coaching: The Disillusioned Learner
A few weeks later, Ernst begins to handle core tasks more independently but starts to experience the harsh realities of lab work: contaminated cell cultures, unstable pH levels, and conflicting data. His early confidence wavers.
Susan shifts gears to coaching. She still provides guidance but starts to pull back from rigid instruction. Instead, she explains why certain steps matter, shares her reasoning behind troubleshooting strategies, and encourages Ernst to begin evaluating options independently.
For example, Instead of telling him exactly how to adjust the pH, she asks,
“What could be throwing off the buffer stability? Let’s think through the possible variables together.”
Here, Susan balances directive and supportive behaviours, i.e. she is still guiding but increasingly building Ernst’s problem-solving capacity.
- What Susan gets out of it: She begins transferring problem-solving responsibility to Ernst, setting him up for independence.
- What Ernst gets out of it: He starts building mental models and learning how to think, not just what to do.
What can go wrong when mismatched: Staying overly directive risks micromanaging. Withdrawing too soon may leave Ernst unsure how to troubleshoot, feeding frustration or imposter syndrome.
This phase is often the heaviest cognitive load for the supervisor, but investing heavily now avoids far larger problems later.
3. Supporting: The Capable but Cautious Performer
Four months in, Ernst executes most experiments confidently. Now, his work expands into designing experiments for a new cell line model. Multiple options exist: should he build the construct using CRISPR or other vectors? How does he control for off-target effects? The choices feel weighty.
Susan dials down directive input and leans into supportive behaviour. She becomes a sounding board, asking open-ended questions, encouraging self-reflection, and helping him prioritise without prescribing every detail.
“You’ve outlined two possible structures for the methods. How do you feel about which one better supports your argument? Walk me through it. What makes you lean toward CRISPR?”
Susan’s role now is to provide psychological safety while encouraging Ernst to take ownership of his decisions.
- What Susan gets out of it: She frees herself from low-level decision-making while staying engaged in higher-level experimental design.
- What Ernst gets out of it: Space to practice independent judgment in real experimental scenarios with the safety net of Susan’s guidance.
What can go wrong when mismatched: Offering too much instruction feels infantilising; withdrawing entirely risks paralysis if Ernst’s confidence dips under pressure.
4. Delegating: The Self-Reliant Achiever
Finally, Ernst fully owns his projects both conceptually and technically. He independently reads literature, synthesises new methods, and proposes modifications to existing protocols based on emerging findings.
Susan moves into true delegation. She still checks in periodically but primarily focuses on strategic conversations. Her role becomes one of mentorship more than supervision, i.e., stepping in only when Ernst requests support.
For example, when faced with new assay adaptations, Ernst independently drafts protocols and only seeks Susan’s expert review for refinement. “Based on the latest paper in Nature Methods, I’ve adjusted the construct design. Can we discuss this before I proceed with cloning?”
- What Susan gets out of it: Cognitive bandwidth for her own research and satisfaction in mentoring a truly independent researcher.
- What Ernst gets out of it: Full scientific ownership, confidence in shaping his research, and mentorship that now supports long-term career development.
What goes wrong when mismatched: Supervisors who struggle to let go may continue inserting themselves unnecessarily, undermining Ernst’s ownership and growth.
Where Supervision Breaks Down
Four traps derail effective supervision most frequently:
1. The Default Mode Trap
In theory, supervising should get easier once you’ve onboarded your student. In practice, many supervisors find that the opposite happens: as time passes, they feel more fragmented, more reactive, and less able to focus on their own work. The interruptions continue. The constant questions don’t stop. And frustration builds on both sides.
This isn’t about bad intentions or poor capability. Often, it’s about supervisors getting stuck in one default style, whether consciously or not.
Some feel most comfortable in full control, staying in the Directing style far too long, even when the student could take on more autonomy. Others, overwhelmed by their own workload, default too early into Delegating, assuming students will “figure it out” without sufficient scaffolding.
Both tendencies lead to what many of us have experienced on both sides of the supervision desk: either over-controlled frustration or premature overwhelm.
2. The Chronic Micromanagement Trap
Some supervisors remain locked in the Directing/Coaching style for far too long. Even after Ernst has run 20 assays, Susan still reviews each buffer prep. Eventually, Ernst disengages, not from laziness, but from never being trusted to own the work.
3. The Linear Progression Trap
The bigger blind spot is assuming progress only moves forward. Development is dynamic. Ernst may be highly independent, running familiar assays. Still, when faced with a new assay or complex data analysis, Susan may temporarily need to shift back to the Directing/Coaching style.
Effective supervisors pick up on these moments early, such as subtle hesitations, recurring questions, or avoidance signals, when temporary scaffolding is needed.
4. The Premature Delegation Trap
The opposite trap is stepping back too early. Susan, pressed for time, assigns Ernst his first manuscript draft without guidance. The result: disorganised drafts, rewrites, and frustration for both.
All three patterns generate the same exhausted reflections:
- Susan may ask herself: “Why can’t Ernst just figure it out by himself by now?”
- Ernst may be thinking: “I’m either being babysat or left to drown.”
Why Situational Leadership Creates a Different Path
Situational leadership in academia prevents these dysfunction cycles because it demands ongoing adjustment. As a supervisor, you are asking yourself:
- Where is this person on this task right now?
- Do they have both the technical competence and the confidence to proceed independently?
- Is my current style scaffolding their growth or blocking it?
By continually recalibrating your leadership style based on the student’s actual developmental level, you move supervision from reactive firefighting to deliberate development.
This model acknowledges the reality that:
- Growth isn’t linear; confidence fluctuates.
- Technical skills and psychological readiness develop at different rates.
- Supervisory style must flex accordingly.
For research teams, it produces the one thing that truly scales academic careers: independent, resilient scientists who no longer need constant intervention.
This is the shift we build at Fast Forward, our seven-week flagship training in Personal Productivity and Project Management, which enables early-career academics to transition from being overwhelmed to confident young research leaders who build independent teams, reduce cognitive overload, and reclaim their capacity for deep, strategic work.
Explore whether Fast Forward is the right training for early-stage researchers at your institute:
Frequently Asked Questions (FAQ’s)
How does situational leadership in academia help improve my supervision skills?
It helps you continuously diagnose your student’s current developmental stage and flex your leadership style accordingly, thus avoiding both premature delegation and chronic micromanagement.
My student is independent in one area but struggling in another. Is that normal?
Completely. Development is task-specific. A student can be fully autonomous on one task while needing close support on another.
How do I recognise when I’ve miscalibrated?
Repeated questions, visible hesitation, or stalled drafts often signal unmet scaffolding needs. Conversely, persistent boredom or disengagement may suggest you’re hovering unnecessarily.
Isn’t this constant adjustment exhausting?
Situational leadership in academia is more about the way you think about leading, i.e. subtle shifts rather than big pivots. It requires conscious effort while you learn, but it prevents far greater rework, frustration, and burnout later.
References
Hersey, P., & Blanchard, K. H. (1969). Management of Organisational Behavior: Utilising Human Resources. Prentice Hall.
Dr. Nadine Sinclair
Nadine is a trusted advisor to corporate and academic leaders and one of the Managing Directors of Mind Matters. Before embarking on her entrepreneurial journey, she was a project manager with McKinsey & Company. A scientist by training and at heart, she conducted her doctoral research at the Max Planck Institute for Biophysical Chemistry. Nadine brings close to 30,000 hours of experience in managing projects for research institutions, research foundations, pharmaceutical and biotech companies (including many Fortune 500) and governments. She continues to build her expertise with over 1,000 hours of project management each year. As a neuro leadership expert, she bridges the gap between science and business practices, leveraging the latest insights from neuroscience and behavioural economics to create breakthroughs for her clients.
