Generative AI tools like ChatGPT, DALL·E and others support a range of tasks for university students, staff, academics and administrators – from drafting emails, to generating images, data models and even lesson plans.
However, GenAI is more than just a personal productivity tool. It represents a deeper, socio-technical transformation in how we work, share knowledge and collaborate across disciplinary boundaries.
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One particularly powerful way to understand GenAI’s impact is by viewing it as what sociologists call a boundary object: a concept that may help interdisciplinary teams communicate more effectively, innovate more freely and rethink traditional roles and workflows.
Boundary objects and why they matter
The term boundary object was coined by sociologists Susan Leigh Star and James Griesemer to describe things – documents, concepts, tools or systems – that are flexible enough to be understood and used in different ways by different groups, but are stable enough to serve as a shared reference point.
Think of a campus map, curriculum guide or a spreadsheet. They mean different things to different people, but help everyone stay aligned. Now imagine this same kind of utility, but turbocharged.
GenAI outputs – whether they’re essays, images, code or even strategic plans – can act as boundary objects in university settings. And unlike traditional IT tools, which often require specialists to interpret them, GenAI makes this kind of shared meaning-making more accessible to non-experts.
A finance officer, lecturer and a curriculum designer can all interact with the same GenAI-generated draft and shape it according to their needs and expertise.
This interpretive flexibility is both GenAI’s strength and its primary challenge.
The power of interpretive flexibility
GenAI doesn’t mean the same thing to everyone.
A teaching and learning centre may see it as a creative assistant for developing course content. An IT department might view it as a data risk. A marketing team could see it as a tool to automate content creation.
These multiple interpretations – what social scientists refer to as interpretive flexibility – can be confusing, but they are also a source of innovation.
Rather than pushing for a single, unified approach to GenAI, universities can benefit from embracing this diversity.
When different departments use GenAI in context-specific ways, they’re not doing it wrong – they’re uncovering different facets of a multifaceted technology. The challenge is to find common ground without forcing uniformity.
How GenAI is more than an IT tool
Traditional IT tools often have clearly defined purposes and outputs – a database stores data, a word processor formats text and so on. Crucially, GenAI’s outputs don’t always fit neatly into existing categories. Are they drafts, recommendations, inspirations or finished products?
This is why classic IT models can often fall short when applied to GenAI. Instead a more nuanced framework, as follows, can help:
Knowledge boundaries, boundary objects and their characteristics (based on Carlile, 2002)
| Type of knowledge boundary | Category of boundary object | Characteristics of boundary objects | |
| Syntactic | Repositories, databases | Representing (enabling a shared, common point of reference) | |
| Semantic | Standardised forms and methods | Representing and learning (enabling knowledge transfer due to shared reference) | |
| Pragmatic | Objects, models, and maps | Representing, learning and transforming (enabling a process where individuals can jointly transform their knowledge) |
GenAI artefacts often span all three. A single prompt and its response can serve as a common reference (syntactic), hold meaning across departments (semantic) and act as a prototype for co-creation (pragmatic). That’s a rare and powerful combination.
How universities can best use GenAI for daily activities
So what does this all mean in practice? Here are some actionable steps for students, staff, academics and support teams to get the most out of GenAI as a boundary object, while avoiding common pitfalls:
1. Think beyond the tech – it’s socio-technical
GenAI is more than a digital assistant – it interacts with people, practices and power structures.
Top tip: Run GenAI introduction workshops that bring together different teams, such as administration, faculty, IT and communications, to surface how each interprets and uses the tool. You’ll likely uncover overlaps – and blind spots – that may surprise you.
2. Use GenAI outputs to bridge communication gaps
Trying to get a faculty member and a graphic designer to agree on a marketing brochure? Use a GenAI draft as a starting point.
Top tip: Treat GenAI outputs like a whiteboard. They’re ideal for provoking feedback and refinement, not to be perfect from the start.
3. Encourage flexible use, not standardisation
Avoid locking GenAI into rigid templates or workflows too early. Its strength is flexibility.
Top tip: Give departments the freedom to adapt GenAI tools in their own way – but also create shared channels, like Teams or Slack, to exchange best practices.
4. Rethink evaluating digital tools
Traditional metrics like efficiency or storage space don’t fully capture GenAI’s impact.
Top tip: Try measuring GenAI success through softer metrics, such as cross-team collaboration, user satisfaction or reduced time-to-draft for key documents.
5. Facilitate interdisciplinary problem-solving
Use GenAI to bring together people who don’t normally work alongside each other.
Top tip: Run GenAI-enabled hackathons or problem-solving sessions. Give teams a complex challenge, like improving student retention, and let them co-create ideas using GenAI tools.
6. Stay ahead of ethical and quality risks
With flexibility comes risk – bias, misinformation or over-reliance.
Top tip: Appoint AI champions or ethics stewards in each department to audit usage and create internal guidance.
7. Make complex knowledge accessible
GenAI can help non-specialists perform complex tasks, but only if it’s applied thoughtfully.
Top tip: Identify repetitive or jargon-heavy processes, such as grant proposal summaries or policy translations, and create GenAI-assisted workflows to simplify them.
8. Expect continuous change – and plan for it
Today’s GenAI will look very different in six months’ time. Build this into your strategy.
Top tip: Set aside time each term to review how GenAI tools are being used and explore new features. Encourage experimentation through low-stakes pilots.
Embrace the ambiguity
GenAI won’t solve all of your university’s challenges, but it will change how you approach them. By thinking of GenAI as a boundary object – one that bridges disciplines, roles and knowledge – it is possible to unlock collaboration and creativity in new ways.
Rather than chasing certainty, higher education institutions should embrace the ambiguity of GenAI as a strength. The more flexible and context-sensitive your approach, the more likely you are to be able to harness its full potential.
After all, in a world of increasingly complex problems, being able to work across boundaries isn’t just helpful – it’s essential.
Lakshmi Goel is dean of the School of Business Administration and Yassine Benrqya is director of accreditation and graduate programmes, both at Al Akhawayn University.
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