Using AI for extended research across Membership knowledge
Generative AI can hugely boost research, but its use demands transparency and responsibility.
Iliana Ivanova, EU Commissioner for Innovation, Research, Culture, Education and Youth
Many of us are now deep in exam and dissertation season and when asked, we probably get the same student response, ‘Oh I used ChatGPT to help write that.’ The next generation are embracing AI at a pace. The Universities claim they have tools to detect AI-generated essays and lecturers can often spot tell-tale lazy signs of AI use. Students widely use it for grammar checks, baseline research, lists and can even set the referencing style. It becomes another tool for their research. The issues arise when the outputs blur academic integrity and promote plagiarism.
These students are leaving colleges and universities to come into the workplace, almost being semi-trained in new technology. Why would a new researcher not use ChatGPT, Baird and others to help them perform better at their job? The AI application guardrails need to be clearly set out at a company or organisational level.
Many organisations with a backlist of important research, technical documents, and whitepapers often get asked how can we extend this knowledge into a member’s own organisation? Incorporate your relevant knowledge with our information to improve our mission.
One challenge is that membership lead organisations often find it hard to localise their content to individual member situations. Take the case today of large Global Standard Development Organisations (SDO) that are used to selling the same simple access to static PDFs of their intellectual property. Their customers and members are increasingly frustrated at how little integration is permitted to have the SDO knowledge near their own relevant data inside their operations. Businesses are already starting to use AI to check compliance, so why can clauses of a Standard not also be validated in context with AI?
There is a deep rooted mistrust of intellectual property reuse (and outdated commercial licensing models) but a growing appreciation of remaining relevant at both ends, the SDO content creator and paying customer. Both are content rich and extended research using AI could offer a bridge to the problem.
This is a point of AI in research. Everyone’s context is different. Research can come from many different pathways to reach the same outcome. AI is an enabler to extracting context at scale and building knowledge graphs of relevancy, potentially aggregating member body content and specific organisational knowledge into a single context driven outcome. These secure extended uses of knowledge arguably build better researchers and organisations utilising the outcomes.
This can only make the membership body’s knowledge more integrated and relevant inside a business or organisation – a mission objective surely. Others have already started the journey.
Librios is an outstanding example of the positive power of AI to organise data. Its ability to absorb over 2000 research reviews and provide informative summaries by pinpointing the key knowledge and learnings, as well and providing a signpost to the original research, is now an invaluable tool for researchers, product developers, policymakers and those with a remit to make agriculture and food production more sustainable.
R Burleigh, Burleigh Dodds Science Publishing, Cambridge UK 2024