GenAI Helps Review Patterns in Student SWOT Projects for Publishing Module (Postgraduate)

I’ve used generative AI to help me review patterns in my marking and feedback given for student assignments in the module, HPSC0044 Publishing as Science Communication. This module is taught at UCL as both a postgraduate (PG) and undergraduate (UG) module through UCL Department of Science and Technology Studies (STS). This post focuses on the postgraduate offer, which includes students studying at Master’s level in degrees such as Publishing MA, Science Communication MSc, and Science, Technology and Society MSc. The core assignment is to produce a SWOT analysis for a medium-scale science publisher. My hope was to identify patterns in what I stress when I’m marking and how those stresses evolve year-to-year. I also wanted to identify patterns in the themes stressed by students in their SWOT analyses and give future students an empirically built sense of expectations, common ground, and routes to distinguish mundane versus outstanding performance. Surely the computational power of GenAI can be put to good use in this area of analysis and reflection.

This review has value as guidance for future students. Some students have access to past work; others do not. Patterns of sharing frequently do not match tutor assumptions. Students rarely examine feedback given to other students, so they rarely have benchmarks to compare their own results with what is provided for others. Neither do students have much general context for ranking their own sense of accomplishment with respect to their compatriots. No surprise that marks alone dominate that comparison: they are easy to compare even though they convey no substantive information. Openness seems to me a warranted response. Given UCL’s laudable approach to student IP, I cannot distribute student work. Neither do I think that’s particularly insightful. Instead, digestion and analysis seem the more fruitful direction of travel. I also do not want to get bogged down in disputes over “fairness” or “bias”. That industry is well advanced in UK higher education, and it is busy across all universities in the UK with its quantitative analysis. Rather, I want to keep the focus on the substantive content exchanges between student and tutor on a particular assignment.

In two posts, I present comparisons using generative AI for mass analysis, compiling information about assignments and feedback in the module HPSC0044. The generative AI used for analysis was GoogleNotebookLM (July 2025). This post analyses results for the postgraduate (PG) module. A separate post analyses results for the undergraduate level version of the module. Content used in the PG analysis was generated during the 2023-24 and 2024-25 academic sessions. I have lightly edited the results to remove meaningless information. I also have removed student identifiers.

1. Assignment in HPSC0044 Postgraduate (PG) level

The postgraduate assignment in HPSC0044 asked students to develop a SWOT analysis for a specific publisher. The assignment developed in two steps:

• status report (formative – 5 minute film) – report on progress in the project, identifying initial successes and areas of difficulty. This is due roughly midterm. Feedback focuses on development and direction-setting. No mark is given.
• SWOT analysis (3000 words) – produce an industry-standard SWOT analysis for the publisher. This is due roughly at the end of term. The analysis contributes 100% to the final mark in the module. Feedback balances mark justification and advice on future improvement.

The assignment worked within the fiction that this SWOT analysis is undertaken as part of the student’s work as an employee in a consultancy company contracted to give such advice. Students are relieved from detailed financial analysis because that information may not be publicly available (though some students show great resourcefulness in legitimately securing it).

Students were given a limited suite of choices for analysis:

• 2023-24 options:
  (a) The Company of Biologists,
  (b) IOP Publishing, or
  (c) PLOS
• 2024-25 options:
  (a) The Royal Society
  (b) National Academies Press
  (c) IOP Publishing
  (d) The Lancet Group

2. Feedback Analysis for Postgraduate Level

2.1 Common Elements in Feedback

I want to analyse the feedback given to students for an assignment. Student projects are given in the file “0044 2025 PG – submissions.pdf”, “0044 2025 PG – submissions.pdf”. Feedback from the tutor given to students is given in the documents “0044 2024 PG – feedback.pdf”, “0044 2025 PG – feedback.pdf”. These are single files merging all feedback to students in a single year. The instruction to students is given in the document titled “syllabus”.

List the 8 most common comments given as feedback. List the 8 most common suggestions for improvement. List 8 of the most unusual or rare comments. Based on the feedback, list 10 items of advice for future students that might lead to a better mark. List 8 items of advice for future students that can define “minimum expectation”. List 8 items of advice that identify elements definitely to avoid in their essays. Use numbered lists. Use British spelling. Ignore the text duplicated in the document templates. Students must remain anonymous, so do not identify the essay or the student, and do not use citations to the essays.

Here is an analysis of the feedback provided to students for the HPSC0044 postgraduate SWOT analysis assignment:

Eight Most Common Comments in Feedback:

1. Overall Solid Analysis: Many students received positive feedback indicating a “good,” “solid,” or “strong overall” analysis.
2. Good Introduction/Conclusion/Executive Summary: The structure’s beginning and end, including the executive summary, were frequently praised for setting a good tone or capturing main points.
3. Effective Use of Data and Research: Feedback often highlighted “excellent use of key sources,” “good use of Clarivate for metrics comparison,” and a display of “broad knowledge in the use of data and other research materials”.
4. Well-Written and Well-Organised Presentation: Students were frequently commended for “clear and concise presentation,” being “well-organised,” and having “well written” essays.
5. Good Range or Breadth of Analysis: Students often demonstrated coverage of “a wide range of subjects” or a “clear breadth across each of the areas”.
6. Helpful Summary Tables: The use of summary tables in the conclusion or at the front of the report was consistently noted as “extremely helpful” or “useful”.
7. Perceptive or Excellent Points Raised: Feedback often identified “good, perceptive material” and that students “make some excellent points”.
8. Strong Section on Strengths: Many analyses were noted for having a good focus on strengths, with strong discussions of elements like open access, peer review, or financials.

Eight Most Common Suggestions for Improvement:

1. Enhance Comparisons with Similar Publishers: The most frequent suggestion was to “develop more comparisons,” such as to Company of Biologists, IOP Publishing, Scientific Reports, or other “similar publishers and their offer”.
2. Incorporate More Data and Metrics: Students were consistently advised to “use more data,” including metrics from Clarivate, or to introduce data through tables and figures.
3. Develop Depth and Breadth of Analysis: Feedback often suggested to “expand to add depth,” “cover a wider range of topics,” or develop certain threads further.
4. Provide Clearer Recommendations and Direction: A key area for improvement was to move from diagnosis to action, explicitly stating “what you recommend” or “what they should do,” and prioritising changes.
5. Condense Writing and Avoid Repetition: Students were advised to “condense some of the writing and avoid repetition” or to improve writing density.
6. Relate More to Module Materials and Themes: Many received feedback to “review the module materials for additional ideas” and “relate more materials from the module into the analysis”.
7. Improve Citations, Especially for Websites: Bibliography and citations needed “more work,” particularly for “sources online”.
8. Focus on Financials and Cost Implications: Suggestions included “digging into financials,” considering “cost implications” for various proposals, and connecting global financial data to the specific case.

Eight Most Unusual or Rare Comments:

1. “Wow! factor”: This comment, indicating “particularly strong insights, creative methods, or otherwise display standout features,” was explicitly mentioned as a positive.
2. “Digging into financials is a real plus”: This specific commendation highlighted the value of financial analysis.
3. “Superb presentation”: A direct and strong positive remark on the overall presentation quality.
4. “First class bibliography”: A rare top-tier assessment of the bibliography’s quality.
5. “Great that you interviewed the CEO”: This direct engagement with an industry professional was noted as highly valuable for the student’s thinking.
6. “The point about the ‘sting’ paper is an excellent one”: A specific content point being highlighted as outstanding.
7. “I have learned from you about this”: A unique acknowledgement from the tutor that the student’s work provided new insights.
8. “This is an unusual overall structure – well done you. It opens the eyes.”: A rare positive comment on a student’s creative structural choice.

Ten Items of Advice for Future Students That Might Lead to a Better Mark:

1. Prioritise Insights and Explanations: Go beyond mere summary, provide in-depth explanations, details, and insights to enhance understanding of key concepts [Source description].
2. Embed Comprehensive Comparisons: Consistently compare your chosen publisher with other relevant entities (e.g., Company of Biologists, IOP Publishing, Scientific Reports, JAMA, BMJ, NEJM, NAS, AIP, RSC) to provide strong benchmarks and add analytical value.
3. Integrate Data and Metrics Throughout: Support all claims with quantitative data, utilising tools like Clarivate and incorporating figures and tables effectively to make your points.
4. Formulate Specific and Actionable Recommendations: Clearly state what the publisher “should do” based on your analysis, offering concrete solutions and a sense of priority for changes.
5. Synthesise Module and Degree Concepts: Explicitly link your analysis to themes and theories from the module (e.g., peer review types, TAT, predatory journals, open access) and broader degree concepts like communication theory or STS theory.
6. Analyse Financial Implications: Go beyond descriptive financial data to analyse investment, cost implications of proposed solutions, and how these affect metrics and business goals.
7. Creatively Explore AI Opportunities: Consider innovative ways AI can offer new services to authors, streamline processes, or generate value from existing materials, providing industry examples.
8. Ensure Professional Presentation: Maintain clear, concise, and well-organised writing with impressive design elements, such as effective bolding and helpful summary tables, comparable to professional publications.
9. Utilise Diverse Research Sources: Demonstrate strong research skills by drawing on a variety of primary (e.g., business reports, interviews) and secondary (e.g., journalists, analysts, Scholarly Kitchen articles) sources for both the case study and comparisons.
10. Refine Writing for Credibility: Write plainly, avoid excessive adjectives and adverbs that inflate claims, and condense writing to ensure clarity and credibility.

Eight Items of Advice for Future Students That Can Define “Minimum Expectation”:

1. Deliver All Task Requirements: Ensure the submission addresses all elements of the SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) and maintains its focus as specified in the assignment.
2. Provide Evidence-Based Arguments: All statements and analyses must be directly supported by evidence, demonstrating a clear relationship between the data and your conclusions.
3. Cover Core Project Elements: Address the fundamental components of the project description, ensuring a comprehensive, even if uneven, coverage of each SWOT section.
4. Organise Bibliography and References Clearly: Ensure references are consistently well-organised, clear, and easy to find, with complete information for each source.
5. Include an Executive Summary: Provide a concise executive summary (under 200 words) that outlines the key points of your analysis.
6. Display Broad Knowledge: Demonstrate a general understanding and broad knowledge in the use of research materials and data.
7. Show Reflection on Module Ideas: Incorporate and reflect on materials and themes from the module’s essential readings and lectures.
8. Declare GenAI Usage: If any assistive AI tools are used, include a clear transparency statement or appendix outlining what tools were used and for what purpose.

Eight Items of Advice That Identify Elements Definitely to Avoid in Their Essays:

1. Generic or Unsupported Claims: Avoid making broad statements that are not specifically tied to your case study or are lacking in evidence.
2. Overuse of Adjectives and Adverbs: Do not inflate claims with excessive adjectives and adverbs (e.g., “enduring influence,” “cutting-edge”), as this can erode credibility; write plainly.
3. Disconnected Financial Data: Do not include financial data without clearly explaining its relevance and specific connection to the analysis of the publisher.
4. Repetitive Content: Avoid repeating information or arguments; condense writing to ensure efficiency and clarity.
5. Unclear or Incomplete Citations: Do not submit work with unclear or incomplete citations, especially for online sources, as this needs work.
6. Inconsistent Formatting: Avoid mixing different formatting styles for elements like summary tables within the same report, as this can reduce comprehension.
7. Introductions That Don’t Introduce: The introduction should set the stage, not develop specific arguments in detail.
8. Over-Reliance on Generative AI Without Human Oversight: While AI can be used for idea generation, do not rely on it for accuracy, prioritisation, or to replace your own voice; ensure you actively edit, expand, and fact-check all AI-generated content. AI may introduce hyperbole, irrelevant information, or incorrect figures.

2.2 Comparison of Feedback 2024 vs. 2025

Compare the 0044 PG feedback for the years 2024 and 2025. What are the differences in emphasis by the marker, if any? Use British spelling. Ignore the text duplicated in the document templates. Students must remain anonymous, so do not identify the essay or the student, and do not use citations to the essays.

The feedback provided for the HPSC0044 SWOT analysis submissions reveals both consistent themes and notable shifts in emphasis between the years 2024 and 2025.

Common Emphases Across Both Years

Across both academic years, the marker consistently emphasised several key areas for improvement and excellence in the SWOT analyses:

1. Data and Metrics: Students were consistently advised to incorporate more data and relevant metrics into their analyses, often suggesting the use of external sources like Clarivate. This was seen as crucial for substantiating claims and providing an empirical basis for their arguments.
2. Comparisons with Peers: A recurring piece of advice was to utilise comparisons with similar publishers or organisations more effectively. This was framed as essential for providing context, benchmarks, and a deeper understanding of strengths, weaknesses, opportunities, and threats within the industry.
3. Application of Module Materials: Students were consistently encouraged to draw more explicitly from the module’s essential readings, themes, and theoretical concepts from across their degree to enrich their analysis.

Differences in Emphasis by Year

While the core principles remained, the emphasis evolved, particularly in response to emerging technological trends and the observed characteristics in student submissions.

1. Stronger Emphasis on Actionable Recommendations: In 2025, there was a significantly heightened focus on moving beyond mere description to providing clear, specific, and actionable recommendations. The marker frequently pressed students to articulate “what to keep or change”, to be “assertive in your recommendations”, and to “take the next step from description and analysis to recommendations”. The expectation shifted towards practical consultancy advice, where students were asked to consider the implications (e.g., cost, speed) of their proposals and justify their recommendations based on the client’s priorities. This emphasis on “recommend solutions” and “what should we do?” became a core message.
2. Prominent Focus on Generative AI (GenAI) Use and Management: This represents the most significant new emphasis in 2025 feedback. While mentioned in 2024, largely in terms of needing a transparency statement and an early observation of “too much GenAI” in one instance, it became a pervasive and detailed area of feedback in 2025.

• Detection of GenAI Characteristics: The marker explicitly noted instances where writing exhibited patterns indicative of GenAI, such as “hyperbole”, “bland out important points”, “irrelevant information”, repetition, and a lack of prioritisation based on current information rather than model training data. The feedback even linked certain formatting choices, like excessive bullet points, to potential GenAI output.
• Guidance on Responsible Use: The marker clearly stated that using GenAI for “idea generation is fine”, but strongly stressed the student’s responsibility to “keep yourself in charge of the project”. The core message was to “add the human touch” through active editing, expansion, fact-checking, and ensuring the student’s own voice and priorities were present. Students were reminded that GenAI “will only do certain types of work and you will want to do much more”.

3. Understanding Organisational Models (Member-driven vs. Shareholder-driven): In 2025, the feedback introduced a notable emphasis on distinguishing between different organisational models, specifically member-driven charities (e.g., IOP Publishing, Royal Society) and shareholder-driven companies (e.g., Elsevier). The marker highlighted that these different models lead to “different drivers, different resources, and different metrics for reward”. Students were encouraged to analyse how the organisation’s fundamental nature impacts its strategy, priorities, and operations, moving beyond a generic “business” perspective. This provided a deeper analytical lens not as prevalent in 2024.
4. Broader Consideration of Publishing Activities: While the focus remained on journals, the 2025 feedback more frequently encouraged students to consider the organisation’s broader publishing portfolio, including books, conference volumes, data, education, and engagement activities. This prompted students to think about how these diverse activities contribute to the overall mission and how they might be leveraged for future opportunities.

In summary, while foundational elements like data utilisation and comparative analysis remained central, the 2025 feedback demonstrated a clear evolution, with a significant new emphasis on the responsible and critical use of GenAI, a stronger push for actionable recommendations, and a deeper exploration of the unique characteristics and broader activities of different publishing organisations.

2.3 Comparison Emphasis Place by Students 2024 vs 2025

Compare the 0044 PG submissions for the years 2024 and 2025. What are the differences in emphasis in 2024 compared with 2025, if any? Use British spelling. Ignore the text duplicated in the document templates. Students must remain anonymous, so do not identify the essay or the student, and do not use citations to the essays.

The submissions for the HPSC0044 module in 2024 and 2025 reveal both consistent areas of focus and notable shifts in emphasis, reflecting the evolving landscape of academic publishing and perhaps a deepening understanding of its complexities among students.

Consistent Emphases Across Both Years

Across both years, students consistently addressed core elements of academic publishing:

Open Access (OA) and Affordability: The transition to Open Access, including Article Processing Charges (APCs) and alternative funding models like transformative agreements (TAs) and waivers, remained a central theme. The ongoing struggle for equity and global reach in OA, particularly for authors from low- and middle-income countries (LMICs), was a recurring concern.

Peer Review Quality and Bias: The integrity, quality, and potential biases within the peer review process were consistently scrutinised. Discussions often included the effectiveness of single-blind vs. double-anonymous review and the need for greater transparency.

Reputation and Credibility: Maintaining a strong reputation and ensuring research integrity remained paramount, often linked to ethical concerns, retractions, and perceptions of quality over quantity.

Differences in Emphasis by Year

While the above themes were ever-present, their nuances and the specific issues highlighted within them changed significantly between 2024 and 2025.

1. Generative AI (GenAI) – From Emerging Threat to Operational Concern and Opportunity:

• 2024 Emphasis: GenAI was primarily flagged as a future threat, with a focus on its potential for fraudulent papers and the current difficulty in detecting AI-written content. The concern was more about academic misconduct and the integrity of submitted work.
• 2025 Emphasis: The discussion around GenAI became much more prominent, comprehensive, and practical. It was explored not just as a threat to integrity but also as a potential tool for publishers’ operations. Students analysed how publishers like The Lancet Group and IOP Publishing were actively researching or implementing AI to improve peer review efficiency, detect plagiarism, enhance data integrity, and even assist with multilingual optimisation and dissemination. There’s an explicit call for responsible AI use, ensuring human oversight and accountability. The rise of “paper mills” is more explicitly linked to technological advancements in fraudulent content creation.

2. Deepening Peer Review Analysis – Beyond Basic Bias:

• 2024 Emphasis: Focus was on the inefficiency of peer review processes (e.g., PLOS’s slow turnaround times) and issues of bias in editor selection or due to factors like English proficiency. IOPP’s early adoption of transparent and double-anonymous review was a notable strength.
• 2025 Emphasis: Submissions delved more deeply into the mechanisms and outcomes of different peer review models. Students extensively compared single-blind, double-anonymous, and transparent peer review, citing specific survey data on author preferences and the proven impact on reducing biases related to gender, geography, and institutional prestige. There was a stronger push for publishers to adopt more inclusive peer review systems across their entire portfolios as a strategic imperative, rather than just an option.

3. Actionable and Strategic Recommendations:

• 2024 Emphasis: Recommendations were often conceptual, such as “PLOS needs to tackle the cost of publication” or “PLOS should consider investing in an internal IT team”. While valid, they sometimes lacked detailed implementation strategies.
• 2025 Emphasis: There was a discernible shift towards more detailed, practical, and strategically aligned recommendations. For example, analyses of The Lancet Group suggest specific ways to improve author experience (e.g., manuscript tracking dashboards) and address high APCs (e.g., World Bank income classification models, GPOA mechanism). For IOPP, suggestions included leveraging specific physics-related emerging fields for new journals and implementing targeted outreach campaigns against predatory publishers. This suggests a greater emphasis on how strategies can be executed.

4. Organisational Models and Business Drivers:

• 2024 Emphasis: While the non-profit status of PLOS and the society-owned nature of IOPP were stated, the direct implications of these organisational structures on their strategic choices were less thoroughly explored in all submissions.
• 2025 Emphasis: The contrast between non-profit/society-owned publishers (e.g., The Royal Society, IOPP, Company of Biologists) and commercial entities (e.g., The Lancet Group, Elsevier) became a more explicit analytical lens. Students often highlighted how their differing “drivers, different resources, and different metrics for reward” influenced everything from pricing strategies and peer review policies to ethical stances and community engagement. The Royal Society’s internal “ossification” due to publishing being secondary to its core mission was a specific point of criticism.

5. Broader Industry Ecosystem and External Pressures:

• 2024 Emphasis: Concerns included market competition, impact of declining print sales, and the rise of predatory journals.
• 2025 Emphasis: Submissions demonstrated a more acute awareness of interconnected external pressures. This included the impact of Plan S policies on transformative agreements, the financial constraints faced by academic libraries, and the growing threat of “science hostility” and misinformation impacting public trust in research. The “publish-or-perish” culture was more directly linked to the proliferation of unethical publishing practices like paper mills.

In conclusion, the 2025 submissions showcased a more sophisticated and forward-looking analysis of the academic publishing industry. While core challenges like Open Access and peer review remained, the emphasis shifted towards integrating the rapid advancements in AI, critically evaluating diverse peer review methodologies, proposing more actionable strategic recommendations, and deeply considering the implications of varied organisational models and the broader, often turbulent, external environment.

2.4 Comparative Declarations About Use of GenAI

For all submission documents, compare the statements about use of AI or generative AI. What are the most common GenAI programmes declared. How are they used? What is their value for the writer?

Students declared using GenAI tools primarily for ideation, structuring, and refining their written content, often acknowledging both the benefits and limitations of these technologies.

Most Common GenAI Programmes Declared

The following GenAI programmes were explicitly mentioned by students in their declarations:

• ChatGPT: This was the most frequently declared GenAI tool. Some students specified “ChatGPT o4” or “ChatGPT4”, indicating use of specific versions.
• Grammarly: Often mentioned for its proofreading and grammar checking capabilities.
• DeepL: Declared for translating content.
• Grok 3: Mentioned by two students for proofreading.
• Other Tools (less common):
  • Undermind and NotebookLM were used for deeper research, holistic briefing articles, and timeline analysis.
  • Sesame was cited for emotional support and brainstorming.
  • Canva was used for fine-tuning AI-generated book covers.
  • Scribbr was mentioned for generating references.

How GenAI Tools Were Used

Students reported a variety of uses for GenAI, primarily in the initial and refinement stages of their work:

1. Idea Generation and Brainstorming: Students frequently used AI to generate initial ideas, potential book and chapter titles, explore potential directions, and brainstorm concepts. One student used it to generate ideas for an author profile to establish expertise.
2. Structuring and Framework Development: GenAI helped in compiling preliminary briefs, providing basic frameworks, and restructuring sections for clarity and better flow.
3. Language and Editing Support: Common uses included refining arguments, improving fluency, correcting grammatical errors, proofreading, checking for clarity, and refining sentences. For non-native English speakers, translation of content was a key use.
4. Content Generation (Specific Elements): AI was used to generate book covers or cover references. Some also used it to produce abstracts or conclusions and to create content for mock-up infographics.
5. Research Assistance: Tools were used to search for books and concepts, compare initial drafts with AI outputs, and suggest specific market breakdowns or citation metrics that were difficult to find. Deeper research, preparing holistic briefing articles, analysing timelines, and revealing key issues were also reported.
6. Conciseness and Word Count Management: One student used AI to condense paragraphs to ensure they stayed within the word limit.

Value for the Writer (and Identified Limitations)

Students identified several benefits from using GenAI, alongside important caveats:

Perceived Value:

• Efficiency and Time Saving: GenAI helped in quickly generating creative ideas, saving time in the initial ideation phase. It expedited initial stages of work such as planning and compiling briefs.
• Ideation and Diverse Perspectives: Students found AI excellent for offering ideas and frameworks and providing diverse perspectives for brainstorming. It could suggest information, like market breakdowns, that was otherwise difficult to find.
• Improved Language and Fluency: AI tools helped improve fluency and correct grammatical errors, particularly assisting non-native English speakers with translation and clarity.
• Structural Organisation: GenAI aided in structuring content, prioritising information, and achieving better flow within the report.
• Conciseness: It was useful for condensing paragraphs to meet word limits.
• Specific Task Support: AI was found to be good at producing abstracts and assisting with visual content creation for covers.

Identified Limitations and Cautions:

• Accuracy and Reliability: Students noted that AI could provide incorrect numbers, necessitating re-verification, and might take information from unreliable sources or interpret it wrongly, requiring extensive fact-checking. In some cases, results from AI were described as “poor” or yielded very limited useful information.
• Lack of Depth and Insight: A common concern was that AI often lacked deeper insight into specific audiences or niche markets. It might not provide the most cutting-edge or deeply tailored strategies, requiring significant further human research and personal insight. Outputs could also be too “generic”.
• Maintaining Human Control and Voice: Many students emphasised that critical thinking, analysis, and final edits were conducted by the author, highlighting the assistive role of AI rather than a replacement for human input. One student explicitly stated that AI “cannot reach the expectation” for certain creative tasks like book cover design, while others highlighted the need to “add the human touch” to the AI’s output.

2.5 Advice for Writing a Better SWOT

Give 8 pieces of advice to students for writing a better SWOT analysis. Use British spelling. Use fewer than 300 words.

Eight pieces of advice for writing a better SWOT analysis

Here are eight pieces of advice for writing a better SWOT analysis, based on feedback:

1. Use plenty of specific data and metrics to support your claims and analyses, as a data-driven approach enhances credibility.
2. Incorporate strong comparisons with relevant competitors or comparators to provide valuable context and benchmarks for your analysis, giving useful perspective on performance and opportunities.
3. Be assertive and clear in your recommendations, moving beyond mere description to suggest specific, actionable solutions and priorities for the client.
4. Ensure specificity and avoid generic statements that could apply broadly; focus on the unique aspects and challenges of your chosen case study.
5. Balance the sections of your SWOT analysis (Strengths, Weaknesses, Opportunities, Threats) in terms of depth and breadth, ensuring no area feels underdeveloped.
6. Demonstrate deep engagement with module materials and key STS themes, applying concepts from the syllabus to enrich your analysis.
7. Maintain a clear, concise, and professional writing style, avoiding overly flowery language or repetition to enhance readability and credibility.
8. Use Generative AI for idea generation but always add your own “human touch” by critically editing, expanding, and fact-checking the content to ensure originality and depth, as AI alone produces bland and generic results.

2.6 Advice for Improving Each Element of a SWOT: Strength, Weakness, Opportunity, and Threat

Each assignment in this package is a SWOT analysis in which students are asked to identify four elements: strengths, weaknesses, opportunities, and threats. List the 8 most common points listed for each element across all submissions. List 8 unusual or uncommon points for each element. What are the most frequently cited sources?

Eight most common points listed for each element

Strengths

1. Commitment to Open Access (OA): Publishers’ dedication to making research freely available.
2. High Quality and Rigorous Peer Review: Emphasis on robust and effective peer review processes.
3. Strong Reputation, Prestige, or Brand Awareness: The long-standing positive perception and influence of the publisher’s name.
4. Global Reach and International Collaboration: The ability to attract authors and readers worldwide and engage in international partnerships.
5. Financial Stability and Profitability: A solid financial position that allows for investment and sustained operations.
6. Effective Use of Metrics (e.g., Journal Impact Factor (JIF), CiteScore): Leveraging performance data to demonstrate success and attract authors.
7. Transformative Agreements (TAs): Successful implementation of agreements that facilitate open access publishing.
8. Benefits of Digital/Online-only Format: Advantages derived from operating exclusively in a digital environment, such as scalability and rapid content deployment.

Weaknesses

1. High Article Processing Charges (APCs): The significant cost of publishing, which can create barriers for authors.
2. Lack of Diversity (Editorial Board, Authors, Staff): Insufficient representation across various demographics within the publishing ecosystem.
3. Issues with Peer Review Process: Problems such as slow turnaround times (TATs), outdated methods, or potential biases.
4. Language Barriers (English-only Publishing): The challenge posed by requiring submissions exclusively in English, potentially alienating non-native speakers.
5. Reputational Issues: Concerns stemming from retractions, past scandals, or a loss of a publisher’s unique selling proposition (USP).
6. Limited/Diluted Scope or Disciplinary Coverage: The publisher’s focus either being too narrow or too broad, potentially affecting its expertise or market appeal.
7. Website or User Experience Issues: Problems with content organisation, outdated design, or difficulty in navigating digital platforms.
8. Lack of Standardisation/Consistency Across Publications: Inconsistent guidelines or platforms across different journals or publishing partners.

Opportunities

1. Artificial Intelligence (AI)/Generative AI (GenAI) Integration: Leveraging AI for various functions like efficiency, translation, summarisation, and integrity checks.
2. Market Expansion (Global South/New Regions): Tapping into unexplored or emerging markets, such as Southeast Asia or BRIC economies.
3. Diversification of Content and Journals: Expanding into interdisciplinary or emerging fields, or creating new journals for specific audiences.
4. Improved Communication and Marketing Strategy: Enhancing outreach through podcasts, social media, and newsletters to increase visibility and engagement.
5. Enhancing the Peer Review Process: Investing in new methods, considering open peer review, or providing compensation to improve efficiency and quality.
6. Financial Solutions and Alternative Funding Models: Exploring ways to reduce APCs, secure funder support, or expand investment portfolios.
7. Increased Accessibility and User Experience: Initiatives like generating lay summaries or providing multi-lingual content to make research more understandable and widely available.
8. Strategic Partnerships and Collaborations: Forming alliances with institutions, other publishers, or regional entities to expand influence and achieve shared goals.

Threats

1. Predatory Publishers, Paper Mills, and Academic Misconduct: The rising challenge of fraudulent publications and compromised research integrity.
2. Competition: The intensifying rivalry from other open access publishers, large publishing conglomerates, and new entrants in the market.
3. Loss of Unique Selling Proposition (USP) or Brand Reputation Damage: The erosion of a publisher’s distinctiveness or a decline in its public image.
4. Financial Challenges: Issues such as declining library budgets, economic recessions, or the difficulty in making open access profitable.
5. Generative AI (AI-related Ethical Issues): Concerns about content blurring, copyright, and the potential misuse of AI in publishing processes.
6. Pirate Websites (e.g., Sci-Hub): Unauthorised platforms providing free access to scholarly articles, posing a threat to publisher revenue.
7. Geopolitical Issues/Political Motivations: External political factors or policy changes that could impact publishing operations or funding.
8. Stagnant Audience/Lack of New Readership: Difficulty in attracting new readers or expanding the user base beyond existing academic circles.

Unusual or Uncommon Points (8 for Each Element)

These points appeared less frequently or offered a particularly distinctive perspective within the submissions:

Strengths

1. Reshaping news services around content: For IOP Publishing, adjusting how news is delivered to align with content strategy.
2. Digital-only format allows for quick pivoting of titles: The ability to rapidly adapt journal titles to emerging topics like Sustainable Development Goals (SDGs).
3. Focus on transparency in peer review tied with peer reviewer training programmes: Highlighting how training reviewers enhances the transparency of the process.
4. Monitoring profitability as a strength: Not just having financial stability, but actively monitoring and managing it as a positive aspect.
5. Ability to attract leading research from significant growth regions like China: Specifically identifying the success in drawing high-quality submissions from particular geographic areas.
6. Independent, self-governing institution: For societies like The Royal Society, the strength derived from being autonomous rather than shareholder-driven.
7. Emphasis on ethics and use of AI to improve submission quality: A proactive stance on using AI to enhance ethical standards in submitted work.
8. Leveraging an extensive peer review network as a foundational strength: The sheer scale and diversity of the peer reviewer pool contributing to quality.

Weaknesses

1. Assumption that cloud services are more environmentally friendly than paper and ink: Questioning a common perception regarding digital publishing’s environmental impact.
2. Open data not functioning as expected: Despite policies, actual open data practices making replication difficult without direct author contact or reverse engineering.
3. Risk of having too wide a range of topics diluting focus and expertise: The potential for a broad journal portfolio to spread resources too thin.
4. Implicit burden of high Impact Factor: How the pressure to maintain a high JIF can lead to editorial biases, neglecting authors from low-income countries or niche topics.
5. Organisational ossification within the publishing arm: A general inflexibility or resistance to change within the publishing operations.
6. “Physical genetics and the cognitive inertia of the discipline”: An unusually phrased point referring to a perceived disciplinary limitation or resistance to change (noted as a potential translation issue in feedback).
7. Unclear/old-fashioned journal names: For example, the use of “Philosophical Transactions A/B” by The Royal Society making journal topics obscure to new authors.
8. Insufficient communication and user support mechanisms in the editorial system: Leading to manuscripts getting ‘stuck’ without authors receiving timely updates.

Opportunities

1. Propose solutions beyond the open science movement, such as science writing academies: Expanding the scope of support for authors beyond traditional open science initiatives.
2. Develop a journal for another audience, specifically students and teachers: Creating new publication avenues to engage younger generations in science communication.
3. Generate lay summaries using AI to increase accessibility for the general public: Utilising AI to simplify complex scientific papers for a broader, non-specialist audience.
4. Explore new publishing models like “bookazines” or anthologies: Repackaging existing content into new products, drawing inspiration from magazines collating articles on specific topics.
5. Disaggregate content and re-aggregate based on issues relevant to specific countries/regions:Customising compilations of research (e.g., related to UN SDGs) for local relevance.
6. Introduce a manuscript tracking dashboard for authors: Providing transparency and real-time updates on a manuscript’s progress through the editorial process.
7. Revival of an “Enterprise Fund” focusing on environmental and sustainable technology investments:A creative financial strategy that ties investment back to the organisation’s broader mission and reputational gain.
8. Creation of an endowment fund to support long-term research projects: A strategic financial move to secure autonomy and sustainability for future initiatives.

Threats

1. Budget constraints imposed by “greedy Big4 conglomerates”: Highlighting the indirect financial pressure exerted by larger entities on smaller publishers.
2. Elon Musk as a reputation threat to The Royal Society: The concern that controversies involving prominent figures associated with the organisation could damage its brand.
3. Growing anti-science sentiment (post-COVID): A broader societal trend impacting trust in scientific publications.
4. Lack of autonomy of science from “economic incentives”: A philosophical critique arguing that the existence of paper mills exemplifies science being unduly influenced by financial motives.
5. Lack of adequate digital preservation: The risk that purely digital articles and their citation chains could be lost if not properly archived.
6. “Postcode lottery” of transformative agreements: Concerns that the uneven adoption of TAs across institutions creates unequal access for researchers.
7. Surge in health misinformation undermining public trust: A direct threat to medical publishers’ credibility in an age of widespread disinformation.
8. Vulnerability to changes in political and public will: For government-funded or publicly-aligned organisations, a reliance on state support can become a risk during periods of public scrutiny or policy shifts.

Most Frequently Cited Sources

The most frequently cited sources across all submissions are:

• Publisher Official Websites and Reports
• Clarivate / Web of Science / Journal Citation Reports (JCR)
• Morris, Sally (et al). The Handbook of Journal Publishing (Cambridge University Press)
• The Scholarly Kitchen
• General Academic Journals and Databases
• ResearchGate and Retraction Watch
• UK and Companies House

2.7 Statistics

From the marks, calculate some statistics, please: mean, standard deviation, median, mode, count, range, minimum mark, and maximum mark. Also list the distribution of marks in the following ranges: 80-100, 70-79, 60-69, 50-59, 40-49, 0-39. Use British spelling. Ignore the text duplicated in the document templates. From the marks, compare the statistics for each year (mean, standard deviation, median, mode, count, range, minimum mark, and maximum mark) for the years 2024 and 2025. What are the trends, if any? Use British spelling. Ignore the text duplicated in the document templates. Students must remain anonymous, so do not identify the essay or the student, and do not use citations to the essays.

Statistical Summary of Marks

• Count: 38 student submissions
• Minimum Mark: 52
• Maximum Mark: 84
• Range: 32 (84-52)
• Mean Mark: Approximately 69.29
• Median Mark: 72
• Mode Mark: 72 (this mark appeared 14 times, more than any other).
• Standard Deviation: Approximately 6.57 (population standard deviation).

Distribution of Marks

Here is the distribution of marks across the specified ranges:

• 80-100 (A++, A+, A): 4
• 70-79 (A-, B+/A): 21
• 60-69 (B, B+): 11
• 50-59 (C, C+, pass): 2
• 40-49 (D, fail): 0
• 0-39 (F, fail): 0

Analysis of Trends

Several discernible trends emerge from comparing the marks across the two academic years:

1. Increased Participation/Submission: There was a noticeable increase in the number of student submissionsfrom 17 in 2024 to 21 in 2025. This suggests a larger cohort or higher submission rate in the latter year.
2. Improved Average Performance: The mean mark saw an increase from approximately 70.18 in 2024 to 73.33 in 2025. This indicates a general improvement in the average performance of the student cohort in 2025.
3. Consistent Central Tendency (Median and Mode): Despite the rise in the average, both the median and mode marks remained consistent at 72 for both years. This suggests that 72 is a very frequently achieved mark and represents a stable central point in the distribution of scores, even as the overall average shifted upwards.
4. Shift in Performance Spectrum:
   1. The minimum mark increased from 52 in 2024 to 55 in 2025, indicating a slight improvement in the lowest scores achieved.
   2. The maximum mark also rose from 80 in 2024 to 84 in 2025, demonstrating that higher top-end performances were achieved in the later year.
   3. The range remained broadly similar (28 in 2024 vs. 29 in 2025), reflecting that the spread between the highest and lowest scores did not change drastically, although it widened slightly due to the higher maximum mark.
5. Slightly Greater Dispersion: The standard deviation increased slightly from approximately 6.43 in 2024 to 7.13 in 2025. This suggests that the marks in 2025 were marginally more spread out around the mean compared to 2024. While the average performance improved, the individual scores exhibited a slightly wider variance.

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