Module overview

This module supports the development of academic writing competence by enabling analysis of differences between informal and formal language use. Participants examine contrasting texts to identify features related to tone, vocabulary, and structure. Artificial intelligence tools (e.g., ChatGPT, Microsoft Copilot, Google Gemini) are incorporated to support linguistic analysis and provide constructive feedback, while preserving independent decision-making. The approach enhances awareness of academic conventions, promotes responsible digital literacy, within a blended lesson and strengthens clarity and precision in written communication.

Section overview

This section introduces the key differences between informal and academic writing. Participants analyze short texts to identify how tone, vocabulary, and structure change across contexts. AI tools are used to support comparison and highlight relevant language features, while learners remain responsible for evaluating the suggestions. The section builds a foundation for developing clearer, more appropriate academic writing in later modules.

Potential AI applications (ChatGPT, Microsoft Copilot, Google Gemini).

Activity Outline

Text Analysis

Participants read two short texts (Text A informal/Text B formal) individually.

The task is to determine which text demonstrates academic characteristics.

Notes are then compared in pairs or small groups.

The need today, therefore, as is evident from above arguments, is to review, revamp and rejuvenate the existing people management (HR) systems, such as the reward and incentive systems, promotions and transfers, training and development programmers, recruitment and selection processes, employee relations, compensation, benefits, and employee motivation such as pay-for-performance, gainsharing and team incentives. They all need to be made more dynamic, effective and in tune with the changing situation. It is also important to keep reviewing how systems are working. Similarly, the systems should be linked to quality service, cost-effectiveness and such other bottom-line issues (Rao, 1996).
Source: Agarwala, Tanuja. “Human Resource Management: The Emerging Trends.” Indian Journal of Industrial Relations, vol. 37, no. 3, 2002, pp. 315–31. JSTOR, http://www.jstor.org/stable/27767793. Accessed 10 Mar. 2026.

Non-academic paragraph Test B
The gap is widening between what is needed from an efficient, effective HR function and what most organizations currently offer. Enhancing employee experience is widely seen as a cornerstone duty of HR, but about 36 percent of employees across Europe and the United States are not satisfied with their current employer. And most HR departments are still far from making full use of the tools and practices available to them, including gen AI, which has been applied at scale to only a small number of HR departments.
Source:https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/hr-monitor-2025

Guiding discussion questions:

• Which text is more academic in nature?
• How does tone, register, and vocabulary differ between the two texts?

Teacher notes

Responses are elicited and recorded by the instructor (e.g., on a whiteboard or flipchart).

AI-Supported Comparative Analysis
Texts are provided digitally for uploading  (see  text above) to an AI tool.
Participants enter a controlled prompt to identify linguistic features:

Model Prompt
‘Compare the language style of Text A and Text B.
Identify features of informal and formal academic writing using bullet points. Do not rewrite the original texts’

AI-Guided Transformation Task
Two sentences from Text A are rewritten to reflect a more formal, academic style. AI provides feedback only; it does not produce the rewritten text.

Model Prompt
‘Evaluate the formality and clarity of this rewrite.
Suggest improvements without altering the intended meaning’

Example
Original sentence from text B

The gap is widening between what is needed from an efficient, effective HR function and what most organizations currently offer

Rewritten sentence
“There is an increasing disparity between the capabilities required of an efficient and effective human resources function and the services currently provided by most organizations.”

Example feedback

Nominalization

The gap is widening → There is an increasing disparity

Participants compare and discuss the feedback received.

Reflection and Ethical Considerations

Individual written reflection on the handout:

• What insights were supported by AI?
• Which language choices were made independently?
• Were all AI suggestions appropriate and accepted? Why/why not?

Pedagogical and Operational Considerations

• Immediate personalized feedback supports faster recognition of academic writing conventions.
• Instructors can shift focus toward critical analysis and evaluation skills.
• The activity is viable in digitally enabled learning environments with basic tool access.
• Initial guidance is required to ensure appropriate, reliable use of AI tools.
• AI integration must remain aligned with learning outcomes, institutional policies, and ethical standards.
• Human oversight is essential to ensure accuracy, accessibility, and academic integrity.
• Ensures compliance with ethical standards:

Verification of AI-generated content
Transparency in tool usage
Accessibility and GDPR compliance
Teacher control over pedagogical decisions

This section supports the learning outcomes by helping participants recognize the differences between informal and academic language. Through text comparison and guided AI feedback, learners develop awareness of tone, register, and vocabulary choices that contribute to academic style. The activities build foundational analytical skills and promote responsible use of AI tools, enabling participants to make clearer and more appropriate language decisions in their own academic writing.

Section Overview

This section builds on the analytical and evaluative skills developed in Section 1, with continued emphasis on tone, register, precision, and appropriate academic style. Participants deepen their awareness of how language choices shape meaning and credibility in academic communication. AI tools support analysis and revision processes while independent judgment remains central.

Potential AI applications (ChatGPT, Microsoft Copilot, Google Gemini).

Activity Outline

Comparative Language Analysis

Participants examine extracts representing different tones within academic contexts (e.g., neutral, critical, cautious).

Cautious

This study has a few limitations. Firstly, we excluded 25% of the households from analysis because of missing information on either income or BMI. It is unlikely that such missing information is related to price elasticity or purchase behaviour [...] however, it may have resulted in some bias for the pooled values across groups. [...] Secondly, the baseline daily energy purchase estimates are sample average estimates that do not consider age or sex of the household members who could have different energy requirements. [...] Thirdly, we used a static model for weight loss based on changes in energy consumption, which might not fully reflect actual mechanisms of weight change. [...] Fourthly, the study does not reflect on the substitution of nutrients alongside changes in energy. For example, reduction in energy from high sugar snacks could lead to substitution of other foods that are lower in energy content but perhaps higher in other nutrients of concern, such as saturated fats or salt. The health impacts of such substitutes should be further analysed and considered in the decision making process around food price policies. Furthermore, the satiety index of sugary snacks can vary greatly: some high sugar snacks could reduce overeating at meals, hence the overall impact of reduced consumption of high sugar snacks would be partly cancelled out by consumption of larger portions during mealtimes. Studies of sugary drinks only would be prone to this phenomenon, as the satiety effect of sugar sweetened beverages is generally low.50 Fifthly, we assumed that all food purchased was consumed, which is unlikely, and some food will inevitably be waste. However, although the link between purchasing and consumption is far from perfect, it is strong (eg,51), and our estimates on the effect of price rises on change in energy purchased is likely to be similar to that on consumption even if absolute values differ.

Source:BMJ 2019; 366 doi: https://doi.org/10.1136/bmj.l4786 (Published 04 September 2019)Cite this as: BMJ 2019;366:l4786

Critical
Research indicates that while digital communication has enhanced the capacity of people to connect across cultures, there are limitations regarding how this communication translates to genuine connection. Smith (2016, p. 75) suggests that digital platforms such as Facebook and Twitter not only benefit users in terms of social connectivity but also help distribute knowledge between people and cultures. Similarly, Cosgrove (2018) found that people who regularly used social media were more likely to engage in activities from other cultures. While Harrison (2017, p. 9) agrees that technology has increased the capacity for people to communicate across cultures, he raises concerns that such forms of communication fail to “foster in-depth relationships” between people and communities from different cultures, because strong connections require situations to be experienced together. Markson (2018, p. 18) likewise warns that digital communication platforms “erode the fundamental principles of cross-cultural engagement” by reducing important and complex aspects of culture to “instant images Source:https://www.uts.edu.au/for-students/current-students/support/helps/self-help-resources/academic-skills/how-write-critically

Neutral

Climate change is widely recognized as a significant factor influencing biodiversity across ecosystems. Research indicates that rising global temperatures and shifting precipitation patterns can alter species distribution, disrupt ecological interactions, and increase extinction risks. However, some species demonstrate adaptive responses, including changes in migration patterns and habitat use. Differences in research methods and geographic focus contribute to variation in reported impacts, but most studies identify climate change as an important driver of ecological change (Intergovernmental Panel on Climate Change, 2021). Source Intergovernmental Panel on Climate Change (2021). Sixth Assessment Report: Climate Change 2021 – The Physical Science Basis.

Reference table

Key linguistic features are identified, including modality, hedging, cohesion, contrastive language, and discipline-specific vocabulary.

Group discussion reinforces terminology and conventions highlighted in Section 1 (e.g., formality markers, lexical choices, structural clarity).

AI-Supported Evaluation

Extracts are provided in a digital format for assisted analysis.

Participants use an AI prompt aligned with ethical guidelines:

Model Prompt

‘Analyse the tone and style of this extract.
Provide bullet-point feedback on academic appropriateness
relating to vocabulary, formality, and clarity’

AI feedback is critically reviewed rather than automatically accepted. Using probing questions (Socratic method)

Language Adjustment

passages or parts of them are revised to adjust tone (e.g., from informal to neutral-academic or overly assertive to appropriately cautious).Revisions are evaluated using a structured checklist referencing Section 1 outcomes:

Academic tone

• Precise and discipline-relevant vocabulary
• Logical sentence structure and cohesion
• Reduction of colloquialisms or subjective language

Reflection and Ethical Compliance

Participants document responses to ensure transparency and accountability:

• Which tone adjustments improved academic quality?
• How did AI support the identification of strengths and weaknesses?
• Were any AI-generated suggestions inaccurate or inappropriate?

Pedagogical and Operational Considerations

• This section reinforces prior learning through iterative skill practice, particularly formality, register control, and contextual vocabulary selection.
• AI feedback accelerates revision processes and enhances learner confidence in applying academic conventions.
• The instructional focus remains on human critique, ensuring responsible adoption of AI in accordance with institutional policies.
• Activities promote independence, accuracy, and ethical integrity in written academic communication.
• Ensures compliance with ethical standards:

Verification of AI-generated content
Transparency in tool usage
Accessibility and GDPR compliance
Teacher control over pedagogical decisions

This activity supports the learning outcomes by helping students’ recognize and adjust tone, style, and clarity in academic writing. Through comparative analysis and guided revision, learners develop greater control over formality, vocabulary choice, and cohesion. AI tools provide structured feedback that encourages critical evaluation rather than passive acceptance, strengthening responsible digital literacy. Overall, the activity enhances participants’ ability to produce more accurate and context-appropriate academic texts.

Section Overview

This section extends the skills introduced in Sections 1 and 2 by focusing on the organisation and cohesion of academic paragraphs. Participants analyse a model text to identify structural features and cohesive language that contribute to clear, logical communication and individually produce an example academic paragraph. Artificial intelligence is used to support teacher preparation and guide participant analysis while maintaining human oversight.

Potential AI applications (ChatGPT, Microsoft Copilot, Google Gemini).

Activity Outline

Teacher-Led Model Text Preparation

A short open-access academic paragraph is selected from a relevant discipline.

Example text

One major cause of urban air pollution is traffic congestion. In many cities, large numbers of private vehicles release harmful gases such as carbon dioxide and nitrogen oxides into the atmosphere. This problem is made worse during peak travel times, when cars remain on the road for longer periods and produce higher levels of emissions. As a result, air quality declines and this can have negative effects on both the environment and public health. For this reason, many governments have introduced policies such as improved public transport systems and low-emission zones to reduce pollution in urban areas (World Health Organization, 2021).

Source World Health Organization. (2021). WHO global air quality guidelines.

The instructor reviews the text to identify cohesive language and paragraph structure is suitable.

An AI tool  (ChatGPT, Microsoft Copilot, Google Gemini). is consulted to assist categorization, using a teacher-controlled prompt:

Teacher Prompt Example

‘Identify the topic, supporting and concluding sentences in this paragraph and cohesive devices and reference chains  used

AI output is checked for accuracy and used to support scaffolding.

Guided Analysis of the Model

Participants read annotate/discuss the paragraph in pairs or small groups to identify what makes it academic in nature.

Features elicited and recorded could  include:

• Cohesive devices (e.g., however, therefore, additionally)
• Reference chains (e.g., this issue, such systems)
• Logical progression (topic sentence → supporting evidence → concluding remark).

Comparison with AI-Generated Output (Students are not shown this until after they have completed this task)

Participants compare their annotations to an AI-generated example paragraph:

Writing Task

Participants write a paragraph on the same topic.

They then seek structured feedback from AI:

This could also be adapted to be competed in sections. Have students create topic sentences with feedback, then supporting sentences and then concluding sentence.

Example topics

• Ai in education
• Climate change
• Honeypot tourism

Example Prompt

‘Provide feedback on structure, cohesion, and reference chains in my paragraph. Do not rewrite it. Identify areas where cohesion could be clearer’ Do not rewrite’

Students discuss

• Which adjustments improved academic quality?
• How did AI support the identification of strengths and weaknesses?
• Were any AI-generated suggestions inaccurate or inappropriate?

Participants consider suggestions critically, reinforcing autonomy and responsible AI use.

Pedagogical and Ethical Considerations

• Strengthens ability to produce structured academic writing.
• Promotes critical evaluation of information from multiple sources, including AI.
• Reinforces correct application of academic conventions and language standards.
• Advances digital and multilingual literacy through guided AI integration.
• Ensures compliance with ethical standards:

Verification of AI-generated content
Transparency in tool usage
Accessibility and GDPR compliance
Teacher control over pedagogical decisions

This activity directly supports the module learning outcomes by strengthening paragraph structure, enhancing critical analysis, reinforcing academic conventions, and promoting informed use of digital tools in multilingual academic contexts.

Module overview

This module extends the foundational academic writing skills introduced in Module 1 by focusing on logical reasoning, claim–evidence relationships, and multi-paragraph argument structure. Participants learn how academic arguments are built, analyzed, and strengthened using analytical frameworks. Artificial intelligence tools (e.g., ChatGPT, Microsoft Copilot, Google Gemini) are incorporated to support evaluation and critique—not to generate arguments—reinforcing responsible digital literacy and critical thinking within a blended learning context.

Section overview

This section develops the ability to organize arguments logically across multiple paragraphs. Participants learn to structure extended arguments using topic sentences, development, counterargument integration, and paragraph-to-paragraph cohesion. AI tools provide structural feedback and support error detection without producing content.

Potential AI Applications (ChatGPT, Microsoft Copilot, Google Gemini).

Activity Outline

Argument Component Identification

The teacher elicits components from the students  that make an argument compelling and academically rigorous. The teacher models the following areas with some examples. (Claim, Evidence, Reasoning, Counterargument).

Participants are given two short argumentative extracts to read (Extract A strong / Extract B weak).

Example text

Extract A — stronger

Social media companies should be required to provide stronger protections for teenage users. Internal platform research, reported by The Wall Street Journal, found that Instagram worsened body-image concerns for some teen girls, while the U.S. Surgeon General has warned that social media can present risks to young people’s mental health. Although online platforms can support connection and self-expression, these benefits do not remove the need for safeguards such as default privacy settings, age-appropriate design, and clearer reporting systems. Because the argument is supported by research findings and official public health guidance, the case for greater regulation is persuasive.

Sources:

U.S. Surgeon General. (2023). Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory.The Wall Street Journal investigation on Facebook/Instagram internal research (2021), as summarized in the Surgeon General’s advisory.

Extract B — weaker

Social media is obviously harmful for teenagers and should probably be banned for anyone under sixteen. Young people spend too much time online, and many of them seem less confident than teenagers in the past. It is clear that social media causes anxiety, poor concentration, and low self-esteem, so governments need to act quickly. Teenagers were better off before these apps became popular, which shows that modern online culture is mainly damaging.

Source note:
This extract is constructed as a weak model for classroom comparison. It is based on a common unsupported argumentative style rather than a published source.

Working individually, students annotate examples of:

Claim (main point), Evidence (data, examples, citations), Reasoning (explains why the evidence supports the claim), Counterargument (addressing opposing views)

Pairs compare their annotations and discuss:

• Which extract presents a stronger argument?
• What features make it stronger?
• What types of evidence are used?

AI-Supported Classification Task

Participants upload both text extracts for an AI-assisted breakdown.

Model Prompt: “Identify the claim, evidence, reasoning, and counterargument in this extract using bullet points. Do not rewrite the text. If uncertain about any element, state the uncertainty.”

Learners check the AI’s classification for accuracy, highlighting: accurate identifications, misinterpretations or hallucinations, missing elements

The teacher might need to pre-teach the concept of hallucinations in an AI context.

Evaluating Strength of Evidence

Participants identify strong and weak evidence in the short argument. The aim is to help learners recognise the difference between well-supported points and vague or unsupported claims.

Identifying Strong vs Weak Support Participants underline:

• strong support (clear facts, examples, or explanations)
• weak support (vague statements, opinions, claims without explanation)

Extension task. Depending on the level of the class, evidence could be further characterized as follows:

Participants categorize each piece of evidence in a sample paragraph:

Empirical, experiential/anecdotal, authoritative, statistical, logical

Example guiding teach prompts:

• Which sentence feels more convincing? Why?
• Which ideas feel unclear or unsupported?

Students discuss their choices in pairs. The teacher elicits student choices and adds them to a whiteboard to initiate class discussion/reflection.

Reflection and Ethical Considerations

Individual written reflection:

• Which argument elements were clearest?
• Where did AI help, and where did it misclassify?
• How can overreliance on AI weaken critical thinking?

Pedagogical and Operational Considerations

• Focus on analytical skills, not AI-generated arguments.
• AI helps accelerate component recognition but requires user verification.
• Teacher guidance ensures productive and responsible critique of AI output.
• Activities support critical thinking, accuracy, and academic integrity
• Ensures compliance with ethical standards:

Verification of AI-generated content
Transparency in tool usage
Accessibility and GDPR compliance
Teacher control over pedagogical decisions

This activity develops learners’ ability to identify the main point of a text and recognize whether ideas are clearly supported. By analyzing short examples and checking their interpretations with AI tools, participants strengthen their critical reading skills and learn how academic arguments are built. This supports clearer, more coherent writing and responsible use of digital tools.

Section development

This section develops the ability to organize arguments logically across multiple paragraphs. Participants learn to structure extended arguments using topic sentences, development, counterargument integration, and paragraph-to-paragraph cohesion. AI tools provide structural feedback and support error detection without producing content.

Potential AI Applications (ChatGPT, Microsoft Copilot, Google Gemini).

Activity Outline

Model Analysis: Multi-Paragraph Argument Structure

Participants examine a short 3-paragraph argumentative text.

Working in groups, they should identify and label each paragraph as follows:

Example text: Should schools regulate student smartphone use?

1. Schools should regulate student smartphone use during lesson time because constant access to phones can reduce attention and interrupt learning. The OECD’s PISA 2022 Results reports that students in many countries become distracted by digital devices in class, and this distraction is associated with weaker learning outcomes. For this reason, restricting phone use during teaching time can support a more focused classroom environment.
2. In addition, the same report suggests that the effects of distraction are not only individual but also collective. When students use smartphones for non-academic purposes during lessons, teachers may need to stop instruction repeatedly, which reduces time for explanation, practice, and discussion. As a result, learning can become less efficient for the whole class rather than only for the students using the devices.
3. However, a complete ban on smartphones in all school contexts may be too simplistic. The OECD also notes that digital tools can support learning when they are used purposefully and under teacher guidance. Therefore, the stronger position is not that smartphones should never be present in schools, but that their use should be carefully managed so that educational benefits are retained while unnecessary distraction is reduced.

Source OECD. (2023). PISA 2022 Results (Volume II): Learning During — and From — Disruption. Paris: OECD Publishing.

Paragraph 1: Introduces main claim

Paragraph 2: Develops supporting evidence

Paragraph 3: Addresses limitations/counterargument

Students identify:

How each topic sentence contributes to argument progression.

The logical relationships between ideas.

The teacher demonstrates and explains what a cohesive device is and its function in academic writing. Students then identify: cohesive devices connecting the paragraphs

Cohesive devices in the text

• For this reason — shows cause and result
• In addition — adds a supporting point
• As a result — shows consequence
• However — introduces contrast / counterargument
• Therefore — signals conclusion based on the previous discussion
• The same report — reference back to the source already introduced
• this distraction — reference to the idea in Paragraph 1
• their use — reference to smartphones in Paragraph 3

AI-Supported Coherence Check

Students’ upload the same model text to an AI tool.

Model Prompt
“Analyze the logical flow of this text. Identify strengths and weaknesses in paragraph organization and cohesive devices, highlighting their function. Do not rewrite the text.”

Learners compare AI feedback with their own notes.

Discussion questions:

• Did AI detect actual coherence problems?
• Did AI overgeneralize?
• What does human analysis add that AI cannot?

Paragraph Chain Reconstruction Task

Participants receive a set of four scrambled paragraphs.

Tasks: In pairs/groups, reorder them logically and justify their chosen sequence. Pairs then request AI feedback on the coherence of their sequence:

Example paragraph chain

A
Ultimately, this multidimensional view of vulnerability underscores that mitigation strategies must be locally tailored rather than universally applied. Adaptive capacity varies according to socioeconomic status, governance structures, and access to technology, meaning that communities with similar climatic exposure may face very different risk profiles. Developing effective policy requires not only understanding physical exposure but also addressing institutional and economic constraints that influence resilience.

B
The concept of vulnerability in the context of climate change extends beyond mere exposure to climatic hazards; it incorporates the sensitivity of a system and its capacity to adapt. Exposure refers to the nature and degree to which a system is in contact with climatic variations, while sensitivity describes how profoundly those variations affect the system. Adaptive capacity, however, determines the system’s ability to adjust, cope, and recover from climatic stresses. Together, these elements shape how significantly a community or ecosystem will be affected by climate perturbations.

C
For example, two coastal communities facing identical increases in sea-level rise may experience vastly different outcomes: one with robust infrastructure, local governance, and emergency response systems may fare better than another lacking these resources. Such differences are not captured by models that only quantify physical exposure. Therefore, vulnerability assessments that omit socioeconomic and governance factors risk oversimplifying the challenges of climate adaptation and producing ineffective or inequitable policy recommendations.

D
Scholars have increasingly adopted a framework that conceptualizes vulnerability as a function of three interrelated components: exposure, sensitivity, and adaptive capacity. This framework emerged from the intersection of climate science, human geography, and development studies. By integrating social and environmental perspectives, it moves away from earlier risk assessments that focused narrowly on physical hazards. Modern vulnerability analysis emphasizes systemic interactions and contextual factors that influence how societies experience and respond to climate change.

Source Füssel, H.-M. (2007). Vulnerability: A generally applicable conceptual framework for climate change research. Global Environmental Change, 17(2), 155–167.

1. D introduces the theoretical framework.
2. B defines the key components of that framework.
3. C provides a real-world example illustrating the ideas in B.
4. A discusses the policy implications of the framework and example.

Model Prompt
“Evaluate the coherence of this paragraph sequence.
Identify any unclear transitions or weak logical links.”

Students discuss the usefulness of the feedback.

Extension task: rewrite topic sentences without changing meaning to improve cohesion based on feedback.

Pairs then request AI feedback on the coherence of their sequence:

Model Prompt: “Evaluate the coherence of this paragraph sequence.
Identify any unclear transitions or weak logical links.”

Students discuss the usefulness of the feedback.

Reflection and Ethical Considerations

Learners write brief responses on:

• Which restructuring choices improved coherence?
• How AI supported (or failed to support) their reasoning?
• What responsibilities do writers have when using AI as a reviewer?

Pedagogical and Operational Considerations

• Strengthens multi-paragraph reasoning and cohesion.
• Enhances independence by encouraging justification of structural decisions.
• Increases awareness of AI limitations in evaluating subtle arguments.
• Supports critical digital literacy and academic integrity.
• Ensures compliance with ethical standards:

Verification of AI-generated content

Transparency in tool usage

Accessibility and GDPR compliance

Teacher control over pedagogical decisions

This activity supports the learning outcomes by strengthening learners’ ability to organize ideas logically across multiple paragraphs. By reconstructing the paragraph sequence, participants deepen their understanding of coherence, cohesion, and effective topic sentence use. AI-generated feedback helps them identify gaps or unclear transitions while encouraging critical evaluation of digital tools. The task develops clearer academic writing and reinforces responsible, informed use of AI for structural review.

Section Overview

This section consolidates the argumentation skills learned in Sections 1 and 2. Participants diagnose weaknesses in sample arguments, refine reasoning, and evaluate AI suggestions critically. Emphasis is placed on fallacy recognition, logical consistency, and independent decision-making in argument improvement.

Potential AI Applications: ChatGPT, Microsoft Copilot, Google Gemini.

Activity Outline

Participants read a short paragraph containing:

• overgeneralization
• unsupported assertions
• vague evidence
• logical leaps
• missing counterarguments

Example Paragraph

The European Commission’s climate policy framework demonstrates that EU efforts to address climate change have largely failed, as emissions reductions have not been achieved quickly enough, showing that current strategies are ineffective overall. Although the European Green Deal outlines ambitious targets for climate neutrality by 2050, reports of “insufficient progress” indicate that Member States are generally unwilling to implement necessary measures. Because some sectors continue to struggle with adaptation, it is clear that the EU’s integrated policy approach cannot work in practice, and therefore more collaborative strategies should be replaced with stricter enforcement mechanisms. Given that climate impacts are increasing across Europe, this further proves that existing policies are inadequate and unlikely to succeed in the future.

Source Adapted from European Commission. Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions: The European Green Deal. European Commission, 2019.

(The paragraph should be prepared and analyzed before the lesson by the teacher).

Groups list all of the problems they find and propose possible improvements. The teacher then elicits the students’ findings.

AI Diagnostic Review

Participants upload the paragraph to an AI tool.

Model Prompt
“Evaluate the strength of the argument in this paragraph.
Identify overgeneralization, unsupported assertions, vague evidence
, logical leaps, and missing counterarguments. Do not rewrite’’

Learners then compare:

• human-identified weaknesses
• AI-identified weaknesses
• overlap discrepancies

This reinforces human oversight. If needed, the teacher will provide any missing information not identified by the students or AI.

Counterargument Integration Task

Participants revise the argument from the example text by adding:

• a counterargument
• a rebuttal
• stronger evidence

They then ask AI to evaluate the revised paragraph (not rewrite it):

Model Prompt
“Evaluate the improvements made to this argument.
Identify any remaining areas needing clarification do not rewrite.”

Students discuss whether the AI’s evaluation is appropriately specific.

Reflection and Ethical Compliance

Participants reflect on:

• which revisions most improved the argument
• what AI overlooked or misinterpreted
• how AI feedback can support but never replace human critical thinking
• ethical boundaries (e.g., AI cannot produce the argument for them)

Pedagogical and Operational Considerations

• Reinforces advanced reasoning and critical evaluation.
• Encourages learners to differentiate between valid and flawed arguments.
• Deepens understanding of responsible AI use in academic contexts.
• Ensures alignment with academic integrity and institutional policies.
• Ensures compliance with ethical standards:

Verification of AI-generated content

Transparency in tool usage

Accessibility and GDPR compliance

Teacher control over pedagogical decisions

This task supports the learning outcomes by helping participants evaluate the logical flow and coherence of multi-paragraph texts. Learners compare AI feedback with their own analysis, strengthening critical thinking and judgement skills. The activity reinforces understanding of paragraph connections, transitions, and argument progression. It also promotes responsible use of AI as a tool for reflection rather than content generation.

Module Overview

This module focuses on developing advanced academic writing skills through the use of sources. Participants learn to evaluate, paraphrase, summarise, and synthesise information from multiple texts while maintaining academic integrity. AI tools are used to support analysis, verification, and reflection, without replacing independent judgment. The module strengthens critical reading, source-based argumentation, and responsible digital literacy.

Section Overview

This section introduces participants to evaluating the reliability, relevance, and credibility of academic sources. Learners practise identifying main ideas, supporting evidence, and potential biases in scholarly texts. AI tools are used to highlight key points and suggest relationships, while learners critically assess accuracy and appropriateness. The section lays the foundation for effective and ethical source use in academic writing.

Activity Outline

Source Review Participants are provided with 2–3 short academic extracts.
Tasks:

• Identify the main idea of each text
• Highlight supporting evidence
• Note any bias or questionable claims

Example texts

Text 1: Climate Policy and Economic Growth

Recent research suggests that climate policies can be aligned with economic growth, particularly through investment in renewable energy and innovation. According to the European Commission, the transition to a low-carbon economy has the potential to create jobs and stimulate technological development. However, it is often argued that most traditional industries will inevitably decline as a direct result of environmental regulation, making such policies economically risky. While some studies indicate positive long-term benefits, others point to short-term disruptions, although these are sometimes overlooked in policy discussions. As a result, it is clear that green growth strategies are the only viable path forward for all economies.

Source European Commission. The European Green Deal. European Commission, 2019.

Text 2: Social Media and Education

The use of digital platforms in education has expanded rapidly, with many educators integrating tools such as Facebook and YouTube into their teaching practices. Research in educational technology suggests that these platforms can enhance student engagement and provide flexible learning opportunities. Nevertheless, it is widely believed that social media generally reduces students’ attention spans and leads to poorer academic outcomes, despite limited consistent evidence across contexts. Some studies report improved collaboration, but these findings are often generalized to all learners regardless of age, discipline, or learning environment. Consequently, digital tools are either entirely beneficial or fundamentally harmful to education, depending on how they are viewed.

Source Selwyn, Neil. Education and Technology: Key Issues and Debates. Bloomsbury Academic, 2016.

Text 3: Urbanization and Sustainability

Urbanization is a defining global trend, with more than half of the world’s population now living in cities, according to the United Nations. Urban areas are often seen as hubs of innovation and efficiency, offering opportunities for sustainable development through improved infrastructure and resource management. However, it is frequently claimed that all large cities inevitably lead to environmental degradation and social inequality, regardless of governance or planning strategies. While evidence shows that some cities have successfully reduced emissions and improved living conditions, these examples are sometimes dismissed as exceptions. Therefore, urbanization should be viewed as a largely negative process that governments struggle to manage effectively.

Source United Nations. World Urbanization Prospects: The 2018 Revision. United Nations, 2019.

Example issues with texts

Text 1 (Climate Policy)

Problem: Overgeneralization / Unsupported claim

“green growth strategies are the only viable path forward for all economies.”

Text 2 (Social Media and Education)

 Problem: Unsupported assertion / Vague evidence

“it is widely believed that social media generally reduces students’ attention spans”

Text 3 (Urbanization)

Problem: Overgeneralization / Logical flaw

“all large cities inevitably lead to environmental degradation and social inequality”

AI-Support
Participants upload one extract to an AI tool.

Model Prompt:
“Summarise the main idea and supporting points of this text. Identify any potential bias or unsupported claims. Do not rewrite the text.”

Learners compare AI output with their own notes to assess reliability and accuracy.

Discussion
Group discussion on:

• Which ideas were well supported?
• Did AI misinterpret any claims or overlook bias?
• How can source evaluation support better academic writing?

Pedagogical and Operational Considerations

• Encourages development of critical reading and analytical skills.
• AI serves as a supportive tool for verification, not content generation.
• Can be used in blended or fully digital environments with basic AI access.
• Teacher guidance ensures learners verify AI suggestions and maintain academic integrity.
• Ensures compliance with ethical standards:

Verification of AI-generated content
Transparency in tool usage
Accessibility and GDPR compliance
Teacher control over pedagogical decisions

This section develops learners’ ability to critically evaluate sources and distinguish reliable from unreliable information. By comparing AI suggestions with their own analysis, participants strengthen critical reading, analytical thinking, and responsible digital tool use. These skills underpin ethical and accurate use of sources in academic writing.

Section Overview

This section focuses on transforming source material into independent writing through paraphrasing and summarising. Participants learn to maintain the original meaning while using their own words and integrating ideas ethically. AI tools are used to provide feedback on accuracy and potential risks of plagiarism, without producing text. The section promotes clarity, precision, and responsible digital literacy in source-based writing.

Activity Outline
Students select 2–3 sentences from a source and rewrite them in their own words. Emphasis on retaining meaning and using discipline-appropriate vocabulary.

The teacher demonstrates paraphrasing by taking a short sentence from a source and rewriting it in different words while keeping the original meaning and including the source.

Example

Original Academic Sentence
According to the European Commission (2019) achieving climate neutrality by 2050 will require significant investment in renewable energy, innovation, and infrastructure across all Member States.

Paraphrased Version
The European Commission (2019) states that reaching net-zero emissions by mid-century depends on major funding for clean energy, technological development, and infrastructure improvements throughout the EU.

Source European Commission. The European Green Deal. European Commission, 2019.

Key strategies are highlighted, such as changing sentence structure, using synonyms, and maintaining academic tone. The example is discussed with the group, showing how to avoid copying too closely and preserve clarity. Students then use these strategies to practice independently.

Summarising Practice
Participants create a paraphrase of a short text (3–5 sentences). Text provided by the teacher.

Example text

Of the more than 1000 bicycling deaths each year, three-fourths are caused by head injuries. Half of those killed are school-age children. One study concluded that wearing a bike helmet can reduce the risk of head injury by 85 percent. In an accident, a bike helmet absorbs the shock and cushions the head. From "Bike Helmets: Unused Lifesavers," Consumer Reports (May 1990): 348.

Source https://owl.purdue.edu/owl/research_and_citation/using_research/paraphrase_exercises/paraphrasing_exercise.html

AI Feedback
Model Prompt:
“Evaluate this paraphrase/summary for accuracy and independence. Highlight any areas that might be too close to the original text.”

Participants reflect on AI suggestions and revise where necessary.

Reflection
Learners answer:

• Why do we paraphrase?
• Were AI suggestions accurate and useful?
• Did any feedback risk over-correction?
• How did they ensure ethical use of sources?

Pedagogical and Operational Considerations

• Reinforces understanding of academic integrity and ethical source use.
• Supports development of independent writing and critical evaluation skills.
• AI is used as a guide, not a substitute for learner production.
• Teacher oversight ensures appropriate, responsible integration of AI feedback.
• Ensures compliance with ethical standards:

Verification of AI-generated content

Transparency in tool usage

Accessibility and GDPR compliance

Teacher control over pedagogical decisions

This section supports learning outcomes by developing participants’ ability to paraphrase and summarise accurately and ethically. It strengthens understanding of academic integrity, independent thinking, and responsible AI use. Learners gain practical skills for integrating sources without misrepresentation or plagiarism.

Section Overview

This section teaches participants to combine information from multiple sources to build a coherent argument. Learners practise integrating ideas, comparing perspectives, and using citations effectively. AI tools support mapping connections between sources and highlighting relationships, while learners retain full responsibility for synthesis. The section develops advanced writing, critical thinking, and source-based argumentation skills.

Activity Outline:

Source Mapping (Teacher-Led Instruction)

Teacher instructions: Provide participants with  short academic sources on the same topic. Guide learners to:

• Identify the main point or claim of each source
• Note supporting evidence
• Identify similarities, differences, or contradictions between sources

Encourage discussion in pairs or small groups to ensure understanding.

Example texts

Source A
Research on remote work suggests that flexible working arrangements can significantly improve employee productivity. A study by Stanford University found that employees working from home demonstrated a 13% increase in performance, which was attributed to fewer distractions and reduced commuting time (Bloom et al. 2015). In addition, remote work has been linked to higher job satisfaction and lower turnover rates. These findings indicate that remote work is generally beneficial for both employees and organizations, particularly in knowledge-based industries. Source:Bloom, Nicholas, et al. “Does Working from Home Work? Evidence from a Chinese Experiment.” Quarterly Journal of Economics, vol. 130, no. 1, 2015, pp. 165–218.

Source B
In contrast, other studies highlight the potential drawbacks of remote work, particularly in relation to collaboration and long-term productivity. According to research conducted by Microsoft Research, remote work environments can lead to reduced communication between teams and weaker professional networks (Yang et al. 2022). The study found that employees working remotely may become more isolated, which can negatively affect innovation and collective problem-solving. As a result, some researchers argue that remote work may ultimately hinder organizational effectiveness despite short-term productivity gains.

Source: Yang, Longqi, et al. “The Effects of Remote Work on Collaboration among Information Workers.” Nature Human Behaviour, vol. 6, 2022, pp. 43–54.

AI-Supported Relationship Mapping

Teacher instructions: Explain how AI can support critical evaluation without producing new text. Participants upload the sources to an AI tool.

Model Prompt:

“Compare the ideas in these sources. Identify similarities, differences, and any gaps. Do not produce a paragraph.”

Learners then verify AI output and discuss:

• Are the relationships identified by AI accurate?
• Are any connections overgeneralised, missing, or misrepresented?

The teacher monitors discussions and provides guidance on interpreting AI suggestions critically.

Ask students to write a short paragraph that:

• Integrates ideas from both sources
• Includes a clear claim and supporting evidence
• Maintains logical coherence and academic tone

After writing, participants request AI feedback on cohesion, clarity, and accuracy (without rewriting the paragraph). Teacher circulates to support analysis and ensure correct understanding of feedback.

Model Prompt:
“Check my paragraph for cohesion, clarity, and logic. Highlight strengths and areas to improve without rewriting.”

Reflection

Ask students to reflect individually:

• How did synthesising multiple sources improve understanding of the topic?
• How did AI support or hinder critical evaluation?
• Were all AI suggestions ethically and academically appropriate?

Encourage sharing reflections in pairs or small groups to reinforce discussion of responsible AI use.

Pedagogical and Operational Considerations

• Develops higher-order synthesis and source-based reasoning skills.
• AI is used to support critical reflection, not to create the argument.
• Teacher guidance ensures ethical and academically responsible integration of multiple sources.
• Supports blended or digital learning environments with AI access.
• Ensures compliance with ethical standards
• Verification of AI-generated content
• Transparency in tool usage
• Accessibility and GDPR compliance
• Teacher control over pedagogical decisions

This section develops learners’ ability to synthesise multiple sources into coherent, evidence-based arguments. Participants practise evaluating, integrating, and citing ideas while maintaining academic integrity. Critical review of AI output reinforces independent thinking, ethical source use, and responsible digital literacy, supporting higher-level academic writing skills.