🔧 Four activities provide opportunities for reflection, critique and discussion.
Artificial Intelligence (AI) emerged in the 1950s as mathematicians, logicians and early computer scientists explored whether machines could replicate human thought. Early pioneers such as Alan Turing argued that intelligence might be understood through behaviour rather than internal processes, leading to the famous question: Can machines think?
The first wave of AI was dominated by symbolic reasoning, inspired by logic and mathematics. Researchers believed that if human reasoning could be decomposed into rules, then computers could simply apply those rules faster and more consistently than people.
Growth of structured knowledge systems
Large rule sets, semantic networks and frames were developed to represent expert knowledge.
Expert systems gained prominence as attempts to encode specialist reasoning directly.
🧠 Early AI viewed thinking as rule-following, not pattern-learning.
Why rule-based AI failed
Expert systems attempted to encode human expertise manually. Thousands of rules were written to diagnose diseases, recommend actions or classify objects. But maintaining these vast rule sets became impossible, contradictions appeared, exceptions multiplied and performance deteriorated as the systems grew.
As systems expanded, contradictions and exceptions increased. Maintaining consistency became impossible. Research into neural networks also stalled after early mathematical criticism, contributing to declining funding.
🧠 This period of stagnation marked a shift towards data-driven approaches.
in the 80s ML emerged when researchers realised that instead of telling computers how to solve problems, they could let them learn from examples. The failure of rule-based systems to scale made data-driven methods increasingly attractive.
Three conditions enabled this transition: 1. Data was being generated at scale in science, finance, and the internet.
2. Statistical methods improved (e.g., Bayesian modelling, maximum likelihood).
3. Computers became fast enough to fit models to real datasets.
ML became the dominant paradigm because it could adapt to messy, uncertain, real-world problems.
ML is based on three main approaches:
| Approach | What It Does | Typical Use Cases | Examples |
|---|---|---|---|
| Supervised Learning | Learns from labelled examples to predict outcomes | classification, regression | regression, random forests, SVMs |
| Unsupervised Learning | Finds structure in unlabelled data | clustering, dimensionality reduction | k-means, PCA, topic modelling |
| Reinforcement Learning | Learns actions through trial-and-error with rewards | control, optimisation, agents | Q-learning, policy gradients |
In the mid 90s, research shifted toward data-driven methods, with probabilistic graphical models and support vector machines (smvs) becoming standard in scientific and industrial settings.
A landmark moment came in 1997 when IBM’s Deep Blue defeated Garry Kasparov, demonstrating the power of specialised computation and statistical optimisation.
As computing power increased, neural networks began to outperform traditional methods.
This shift enabled major breakthroughs in computer vision, speech recognition and natural language processing.
🧠 Deep learning transformed AI performance across multiple domains by learning rich features automatically.
In the early 2000s Natural Language Processing (NLP) emerged as a field of AI that focuses on understanding and generating human language. It focused on tasks such as translation, sentiment analysis, summarisation and question answering.
graph TD
RNN["RNN: Sequential, short-term memory"] --> A["Weak long-range memory"]
LSTM["LSTM: Gated cells, long-term memory"] --> B["Heavy to train"]
GRU["GRU: Simplified LSTM, efficient"] --> C["Still sequential"]
CNN["CNN: Detects local patterns, fast"] --> D["Limited global context"]
Language is challenging because meaning depends on context and relationships across sentences. Early deep learning models attempted to address this.
By the mid 2010s, sequence models had limitations:
🔁 Researchers needed models that could look at text blocks all at once.
Transformers introduced self-attention, letting models compare all words in a text block simultaneously.
flowchart LR
A[Input] --> B[Embeddings plus Positional Encoding]
B --> C[Multi-head Self-Attention]
C --> D[Feed-forward Layers]
D --> E[Transformer Blocks]
E --> F[Output]
🚀 This unlocked large-scale training and dramatically improved performance across language, vision and multimodal tasks.
Transformers are capible of reasoning and generalisation by predicting predicting the next most likely token based on previous context.
sequenceDiagram
participant U as Prompt
participant M as LLM
U->>M: Provide prompt
M->>M: Compute attention
M->>U: Output token
loop Generation
M->>M: Update context
M->>U: Emit next token
end
✨ Given a large enough training set, this allows them to generate responses the appear thougtful.
Scaling laws reshape AI research
Transformers trained through self-supervision on large datasets have emerged as the most common general-purpose models capable of adaptation across diverse tasks.
| Property | Description |
|---|---|
| Scale | Trained on trillions of tokens |
| Generality | Perform across domains |
| Transferability | Effective adaptation with small datasets |
🧠 What we think of as AI now are essentially just large scale transformer models based on text.
Foundation models
🚀 Foundation models shifted AI from task‑specific systems to general‑purpose platforms
AI began with rules but moved toward learning. Deep learning enabled rich representations, and sequence models advanced language processing. Transformers removed key bottlenecks, allowing the creation of foundation models and LLMs.
🌐 Modern generative AI is the result of many incremental breakthroughs.
Which AI tools (if any) have you used so far in your research workflow? Select one or more options in the poll provided. If you have not yet used any AI tools, indicate “None” and reflect on where AI might help.
Generative AI offers researchers new ways to accelerate analysis, explore ideas and reduce cognitive load across the research lifecycle.
flowchart LR
A["Accelerate Literature Review"]
B["Explore Ideas & Hypotheses"]
C["Streamline Data Analysis"]
D["Assist Writing & Visualisation"]
E["Enhance Dissemination & Communication"]
A --> B --> C --> D --> E
🚀 There are pontential uses of AI at every stage
Starting a project begins with understanding what has already been explored. AI tools can scan thousands of papers, summarise findings, and highlight gaps in knowledge. They can also map citations to show how ideas connect across disciplines.
| Tool | What it offers |
|---|---|
| Semantic search | Surfaces relevant papers quickly |
| Summarisation | Condenses long articles into key points |
| Citation mapping | Reveals clusters and gaps in the literature |
🔎 AI accelerates discovery, but researchers must judge relevance and quality.
Once the landscape is clear, researchers move to generating ideas. AI can suggest related concepts, simulate scenarios, and reframe ideas into testable hypotheses. It acts as a creative partner, widening the space of possibilities.
💡 AI sparks creativity, but researchers decide which hypotheses are worth pursuing.
Data analysis is often time‑consuming.
AI assistants reduce friction by generating boilerplate code, spotting errors, and suggesting statistical methods or visualisations.
AI can:
⚙️ AI accelerates routine coding, while researchers ensure methodological rigour.
Communicating results requires clarity and precision.
AI tools can draft sections, polish language, and generate visual summaries. They help create abstracts, captions, and even graphical abstracts, allowing researchers to focus on interpretation.
| Output | AI Support |
|---|---|
| Abstracts | Drafting structure and flow |
| Language | Polishing clarity and grammar |
| Figures | Captions and graphical abstracts |
| Slides | Auto‑generated from paper content |
✍️ AI reduces friction in communication, but the researcher’s voice remains central.
The final stage is sharing findings.
AI can adapt outputs for different audiences, from journal formatting to lay summaries and policy briefs.
It helps amplify reach and accessibility without replacing researcher responsibility.
Examples include: - Formatting preprints into journal templates
- Creating lay summaries for non‑specialist audiences
- Generating social media snippets for outreach
- Drafting policy briefs or dashboards for decision‑makers
🚀 AI amplifies impact, while researchers safeguard accuracy and credibility.
AI introduces new vulnerabilities into research workflows where accuracy and integrity are essential:
| Risk | Description | Implications for Research |
|---|---|---|
| Incorrect information | AI may hallucinate or fabricate facts | Misinterpretation, false leads |
| Bias | Models inherit bias from training data | Distorted or unfair outcomes |
| Copyright issues | Generated text may echo sources | Plagiarism or licensing violations |
| Data protection | Risks when inputting sensitive information | Potential legal or ethical breaches |
| Skill erosion | Over-reliance weakens critical thinking | Reduced analytical capacity |
| Accountability | Researchers remain responsible | Need for verification and documentation |
These issues need to be addressed for AI to be in research
flowchart LR
A[Data Input] --> B{Confidential?}
B -->|Yes| C[Risk: Data Protection]
B -->|No| D[Proceed]
D --> E[AI Output]
E --> F{Accurate?}
F -->|No| G[Risk: Hallucination or Bias]
F -->|Yes| H[Use with Verification]
H --> I[Research Output]
🔐 Researchers should treat AI as a tool that requires oversight, not as an authoritative source.
Generative models can produce outputs that sound fluent and convincing, yet contain factual errors or invented details.
This risk is especially high when dealing with technical or niche topics where the model may lack reliable grounding.
⚠️ All AI‑generated claims must be verified against trusted sources before inclusion in research.
Because models are trained on historical data, they inevitably absorb uneven representation and social biases.
These can surface in analyses, summaries, or even in the framing of questions.
| Source of bias | Example impact |
|---|---|
| Gender imbalance | Skewed assumptions in social science outputs |
| Cultural dominance | Underrepresentation of minority perspectives |
| Historical stereotypes | Reinforcement of outdated or harmful narratives |
🧩 Researchers must actively identify and mitigate bias to ensure fair and balanced outputs.
Generated text may unintentionally echo or replicate wording from training data.
This raises questions about originality and intellectual property, particularly in academic contexts where citation and authorship are critical.
Paragraph example:
AI‑generated drafts can resemble existing publications without clear attribution.
To maintain integrity, researchers should:
📖 Proper citation and clear authorship are essential to avoid plagiarism and protect originality.
Submitting sensitive or unpublished information to external AI tools can expose researchers to institutional or legal risks.
Confidential datasets, draft manuscripts, or proprietary results should not be processed without approval.
🔐 Treat unpublished research data as confidential unless explicitly approved for AI processing.
Over‑reliance on AI for interpretation, coding, or summarisation can gradually reduce researcher expertise.
Skills such as critical reasoning, methodological design, and precise writing may weaken if delegated too often.
Paragraph example:
AI should be viewed as a supplement, not a substitute. Researchers must continue to practice core skills to maintain independence and ensure that AI outputs are critically evaluated.
🧠 AI should support, not replace, the development and application of critical research skills.
Researchers remain accountable for all outputs, regardless of AI involvement.
Good practice includes verifying results, documenting AI contributions, and maintaining audit trails that show how tools were used.
📑 Accountability rests with the researcher; AI is a tool, not a shield from responsibility.
Generative AI can accelerate research and stimulate new thinking when used responsibly.
However, careless use can introduce errors, bias and ethical risks. Responsible integration requires caution, transparency and preservation of core analytical skills.
🧭 The goal is not to avoid AI, but to use it responsibly within research practice.
Summarise the UoN use of ai policy in 6 built points
TASK In small groups, spend 10 minutes critiquing the AI summary. Identify useful elements of the summary. Highlight any factual errors, missing context or biases. Discuss whether the AI cited sources and how you would verify its claims.
The opportunities and risks of generative AI lead naturally to a broader question:
How can researchers use these tools responsibly?
Academic research depends on accuracy, integrity and respect for participants and data. Generative AI does not remove these responsibilities; it changes how they must be upheld.
Generative AI influences how researchers read, write, analyse and interpret information.
Because these systems produce fluent and confident output, errors can spread quickly if unchecked.
⚖️ Ethical AI in research protects people, data and scientific credibility.
Key reasons ethics matters include responsibility, trustworthiness, fairness and compliance with institutional and legal requirements.
| Principle | What It Means | Why It Matters |
|---|---|---|
| Transparency | Clearly state how and why AI was used; document prompts, versions and settings. | 🔍 Supports reproducibility and builds trust. |
| Accountability | Researchers remain responsible for accuracy, originality and ethical compliance. | Ensures AI does not replace critical oversight. |
| Fairness and Bias Awareness | Models may inherit or amplify bias; outputs must be examined critically. | 🧭 Prevents distorted or inequitable research outcomes. |
| Privacy and Data Protection | Do not enter sensitive or unpublished data into external AI tools. | 🔐 Protects participants, institutions and legal compliance. |
Institutional policies - Universities provide guidance on acceptable uses of AI, data protection rules and disclosure requirements. Researchers should follow local expectations.
Regulatory frameworks - Large projects, especially in health or social science, may be affected by legislation such as GDPR or emerging AI-specific regulations.
Publisher and funder expectations - Journals and funders increasingly require clear statements describing AI use, confirmation that authors remain responsible for content and proof that citations and claims were manually verified
| Principle | What to Do |
|---|---|
| 1. AI as aid, not replacement | Use AI for drafts/ideas, not decisions. Always review outputs. |
| 2. Verify everything | Cross-check claims, numbers, stats, citations with trusted sources. |
| 3. Keep human oversight | Review AI code/analysis, validate assumptions and data integrity. |
| 4. Document use | Record tools, versions, prompts, and validation steps for transparency. |
| 5. Respect IP | Ensure originality, paraphrase, and cite correctly. |
| 6. Be transparent | Disclose AI use in papers, theses, and presentations. |
Ethical issues can arise at most stages of the research lifecycle:
flowchart LR
A[Research Input Data] --> B{Sensitive or<br/>Unpublished?}
B -->|Yes| C[Do NOT use External AI<br/>• Use institutional tools<br/>• Apply anonymisation]
B -->|No| D[Proceed with Caution]
D --> E[AI Processing Step]
E --> F{Output Verified<br/>by Researcher?}
F -->|No| G[Ethical Risk Detected<br/>• Hallucination<br/>• Bias<br/>• Mis-citation<br/>• IP issues]
G --> H[Apply Governance Controls<br/>• Cross-check sources<br/>• Document corrections<br/>• Re-evaluate prompt]
F -->|Yes| I[Validated Output]
I --> J[Integrate into Research Workflow<br/>• Cite AI use<br/>• Maintain audit trail<br/>• Ensure reproducibility]
If in doubt, dont use AI!
Generative AI is evolving rapidly. Responsible use requires ongoing attention to transparency, verification and respect for data. A helpful guiding question is:
Does using AI improve the rigour, clarity or integrity of the research? If not, it should not be used.
🧭 Responsible AI strengthens scientific credibility and public trust.
Reflect individually on the question: “In which parts of your research workflow would you feel comfortable declaring AI use?” In groups, discuss tasks where disclosure is mandatory (e.g. writing, peer review) and tasks where AI should be avoided.
Create a summary listing stages of the research lifecycle (e.g. literature review, data analysis, writing, peer review). For each stage, decide whether AI could be used and record ethical considerations (bias, consent, data privacy, authorship) and any relevant policies.
Reconvene for a live Q&A. Bring your examples and concerns for discussion. The facilitator will address common misconceptions and challenges, drawing on institutional guidance and publisher policies.
As AI becomes more integrated into research workflows, understanding data security and anticipating future developments is essential.
The most immediate risk associated with AI tools is how they treat sensitive, identifiable or unpublished information. Many commercial AI platforms store user inputs and may reuse them for model improvement unless configured otherwise.
🔐 Once data is submitted to a public AI tool, researchers may lose control of how it is stored or used.
Examples of sensitive data include personal or clinical information, proprietary datasets, unpublished manuscripts, reviewer comments and identifiable qualitative data.
Commercial systems often:
This creates legal, ethical and reputational risks.
Universities provide guidance on approved tools, data protection rules, anonymisation requirements and GDPR-compliant workflows. Researchers should stay aligned with these policies.
Some institutions offer privacy-preserving AI environments, such as:
These allow safe experimentation while keeping data under institutional control.
Unless a platform explicitly guarantees no storage, no training and full isolation, assume that uploaded information is retained.
🛡️ A simple rule: if you cannot email the data externally, do not upload it to an AI platform.
| Area | Risk | Recommended Approach |
|---|---|---|
| Confidential data | Loss of control or reuse in training | Use secure internal tools; anonymise or pseudonymise |
| Commercial platforms | Unknown retention policies | Avoid uploading sensitive information |
| Regulation | GDPR and ethics requirements | Follow institutional guidance |
| Reproducibility | Lack of documentation | Maintain audit trails of AI use |
flowchart LR
A[Research Data] --> B{Contains Sensitive Information?}
B -->|Yes| C[Do Not Use Public AI Tools]
C --> D[Use Secure Institutional Platform]
B -->|No| E[Proceed with Caution]
E --> F[Review Tool Policies]
F --> G{No Retention?}
G -->|Yes| H[Use Tool]
G -->|No| I[Consider Safer Alternative]
🔍 Understanding how tools handle data helps avoid unintentional breaches.
AI capabilities continue to evolve rapidly, and research environments, funders and policymakers are responding in kind.
Examples include:
These frameworks are designed to protect individuals and ensure ethical, transparent use of AI.
UoN provides the following guidence on use of AI, this will continue to evolve as AI becomes more embedded in teaching and research.
| Area | Current Guidance |
|---|---|
| Approved tools | Use only permitted AI platforms (e.g., Microsoft Copilot via UoN login). |
| Disclosure | Always acknowledge AI use in assessments and research outputs. |
| Acceptable use | AI is prohibited in assessments unless explicitly allowed; false authorship = misconduct. |
| Skills training | Staff and students encouraged to complete UoN AI training (Moodle module, SharePoint resources). |
If you are not from UoN your universities policies may be different!
Researchers will increasingly need to understand:
🎓 Responsible AI use is becoming as essential as statistical literacy or good data management.
The role of AI in academia will expand. Researchers should aim to:
Generative AI offers powerful opportunities, but only when used with careful judgement and clear ethical awareness.
🧭 The future of AI in research will be shaped by how responsibly it is used today.
“How confident are you now about using AI responsibly in your research?” Choose from options such as very confident, somewhat confident, neutral or unsure.
AI supports research via literature search, coding, analysis and translation, but outputs may be biased, incorrect or insecure.
Key opportunities: efficiency, creativity, hypothesis generation and reduced repetitive workload.
Main risks: hallucinations, bias, plagiarism, copyright/IP issues, data breaches and loss of critical skills.
Ethical use requires transparency, accountability, fairness, privacy, and disclosure—AI is never an author.
Use secure, approved tools; avoid sensitive data sharing; follow institutional, funder and publisher policies.