23 Unit 4 scenarios: Datafication and GenAI
School context
Tutor prompts inserted into Microsoft Copilot:
Generate a scenario based on the below.
Here is some context for a unit which is about datafication and GenAI in Education.
Key concepts we will need to explore in the scenario are data literacy, opportunities and issues of datafied education, AI literacy, GenAI amplifying inequalities, learning supported by GenAI and assessing learning.
General observations that apply:
- Student using social media routinely to stay in touch with friends.
- Always with their phone and on their phone.
- A lot of choice using software, apps and other digital platforms.
- Taking photographs routinely, also editing these.
- Using a range of social media apps.
- Many play multiplayer games.
- Also creating online content, such as videos.
- Doom scrolling.
- Connected living but disconnected learning.
- Not sure how to use digital technologies for their learning.
- Students rely primarily on their tutors for feedback.
This scenario would relate to teachers and students in school. And here is what is happening: Please use the above and below to build a scenario. Do NOT provide solutions. Include discussion points and discussion points linked to the module learning outcomes.
The generic module learning outcomes are:
LO1: Critically assess key contemporary debates of digital technology, education and society in your professional context.
LO2: Apply specific learning theories relevant to digital education in your professional context.
LO3: Critically reflect on a range of digital technologies and practices in your professional context.
| Students in schools | Teachers in schools |
| · Ethical implications, critical use, collaboration and role of human
· Student feeling overwhelmed with amount of work to read and complexity. · Student has limited contact to other students. · Is in a large class and teacher doesn’t know students well. · Teacher works well. With more confident students and high achievers. · Many others struggle. Help from teacher minimum. · Student had questions and is seeking answers. · Turns to generativeAI.
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· Teachers spending time collecting data. Tracking students’ performance. Collecting evidence.
· Reduced time to interact with students. · Teachers use lots of data around students’ performance. · A lot of tracking around the students’ performance. A lot of time spends adding data. · Less time for learning relationships. · Introduce interventions based on data, also group work. · Preparing for exams. Performance in exams very important for school. · Teachers prepare students for exams throughout all years. · Focus on content delivery. · Multiple subjects. · Not always easy to interpret the data. · Success of students measured by data. · Making changes to curriculum based on data aiming to reduce performance of students. · Data monitored by schools.
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Copilot generated scenario: Datafication and genAI in a school setting
Scenario: “Data-Rich, Time-Poor: Navigating AI in the Classroom”
Setting:
A large secondary school in a city with a diverse student population. The school is under pressure to improve exam results and uses a data-driven approach to monitor student performance.
Characters:
- Mr. Taylor, a Maths teacher responsible for three large classes, known for his structured lessons and focus on exam preparation.
- Aisha, a Year 11 student who is creative and digitally fluent but struggles with maths.
- Liam, a high-achieving student who frequently uses GenAI tools to extend his learning.
- School leadership team, focused on performance metrics and data dashboards.
What’s Happening:
Mr. Taylor spends much of his time entering data into the school’s performance tracking system. He uses this data to identify students who are underperforming and assigns targeted interventions, such as additional worksheets or group tasks. However, his administrative workload leaves limited time to build meaningful relationships with students or to understand their individual learning needs.
Aisha finds the pace of the class overwhelming. She often has questions but feels reluctant to ask them in front of her peers. To cope, she turns to a GenAI chatbot on her phone to explain concepts in simpler terms. Sometimes it helps, but other times the explanations are confusing or inaccurate. She doesn’t know how to establish if the information is reliable or not.
Liam uses GenAI to generate practice questions and model answers. He feels confident in filtering useful content and uses it to deepen his understanding. He’s also started using AI to draft essays and solve problems, but doesn’t always disclose this to his teachers.
The school leadership team relies heavily on student performance data to inform curriculum decisions and teaching strategies. However, they don’t always consider how students are using GenAI tools or the ethical implications of AI in learning. There’s no formal guidance on AI use, and access to devices and internet varies widely among students.
Discussion Points:
General Reflections:
- How does the use of GenAI reflect or reinforce existing inequalities in access, confidence and support?
- What are the risks of relying heavily on performance data to shape teaching and learning?
Linked to LO1 (Critically assess key contemporary debates…):
- How does this scenario reflect current debates around the role of AI in education?
- What are the ethical and societal implications of datafication and AI use in schools?
Linked to LO2 (Apply specific learning theories…):
- How might constructivist or connectivist theories inform more meaningful uses of GenAI in learning?
- In what ways could experiential learning help students develop AI literacy and critical thinking about data?
Linked to LO3 (Critically reflect on digital technologies and practices…):
- How can teachers critically reflect on their own use of data and AI tools in the classroom?
- What are the limitations of current digital practices in supporting equitable and ethical learning?
- How might schools support students in developing data literacy and critical AI use?
University context
Tutor prompts inserted into Microsoft Copilot:
Generate a scenario based on the below.
Here is some context for a unit which is about datafication and GenAI in Education.
Key concepts we will need to explore in the scenario are data literacy, opportunities and issues of datafied education, AI literacy, GenAI amplifying inequalities, learning supported by GenAI and assessing learning.
General observations that apply:
- Student using social media routinely to stay in touch with friends.
- Always with their phone and on their phone.
- A lot of choice using software, apps and other digital platforms.
- Taking photographs routinely, also editing these.
- Using a range of social media apps.
- Many play multiplayer games.
- Also creating online content, such as videos.
- Doom scrolling.
- Connected living but disconnected learning.
- Not sure how to use digital technologies for their learning.
- Students rely primarily on their tutors for feedback.
This scenario would relate to educators and students at university. And here is what is happening: Please use the above and below to build a scenario. Do NOT provide solutions. Include discussion points and discussion points linked to the module learning outcomes.
The generic module learning outcomes are:
LO1: Critically assess key contemporary debates of digital technology, education and society in your professional context.
LO2: Apply specific learning theories relevant to digital education in your professional context.
LO3: Critically reflect on a range of digital technologies and practices in your professional context.
| Students at university | Educators at university |
| · Students feel survey fatigue.
· Not always sure why they are asked to complete a survey. · Students avoid completing surveys. · Sometimes students use surveys to complain about their tutors and their experience on a module or the programme. · Students know that there is surveillance in place and that their moves are monitored when housing digital devices and institutional platforms. · Students using GenAI to manage learning load, to get feedback on their work without being judged. · Students avoid stating openly that they are using GenAI in their learning. · Students are fearful that educators will blame them for cheating in their assignments.
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· Learning analytics not used widely at module level. Interpretations manually based on educators reflections, observations and literature if critically reflection on the data.
· Data collected from students routinely like for example use of library, logging into VLE. Also for evaluating usage of specific digital tools. · Educators evaluate their modules and programmes routinely using survey instruments. · Survey instruments also used at institutional level and nationally and internationally results inform league table position of universities. · Data collected also used to evaluate programmes, teaching quality and inform decision making about the future development of programmes. · Metrics very important for universities. · Success of students measured by data. · Changes to curriculum made based on data and evaluations by educators. · Data monitoring by others in the university. · EdTech collection of data collection and interpretation build in. · Measuring use of digital technologies and tools. · Not all educators are familiar with GenAI. Don’t know how best to use it in their teaching. · Not open and transparent about their use of GenAI. Hide it from students when they use GenAI. · Educators avoid using GenAI and may ask students not to use it at all.
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Copilot generated scenario: Datafication and genAI in a university setting
Scenario: “Invisible Data, Silent AI: Learning in the Shadows”
Setting:
A large, research-intensive university with a strong emphasis on digital transformation and performance metrics. Students and staff are increasingly aware of the presence of data collection and AI tools, but conversations about their use remain limited and cautious.
Characters:
- Dr Evans, a senior lecturer in Business Studies, known for her structured lectures and high expectations.
- Maya, a second-year undergraduate student juggling part-time work and studies, often using GenAI to manage her workload.
- Tom, a student who avoids using GenAI due to fear of being accused of cheating.
- University administration, focused on data-driven decision-making and institutional rankings.
What’s Happening:
Dr Evans is preparing her end-of-semester module evaluation. She encourages students to complete the online survey, but participation is low. Some students, like Maya, feel fatigued by constant requests for feedback and don’t see how their input leads to change. Others, like Tom, use the survey to express frustration with the lack of clarity around GenAI policies.
Maya frequently uses GenAI tools to summarize readings, generate essay outlines, and get feedback on her writing. She finds it helpful, especially when she’s unsure how to start an assignment. However, she never mentions this to her tutors, fearing it might be seen as academic misconduct.
Tom, on the other hand, avoids GenAI entirely. He’s heard mixed messages from different lecturers—some warn against it, others ignore it. He’s confused about what’s allowed and feels disadvantaged compared to peers who use AI tools discreetly.
Meanwhile, Dr Evans uses institutional data dashboards to track student engagement—logins to the VLE, library usage, and attendance. She notices a drop in participation but isn’t sure why. She’s unaware that some students can’t access videos due to bandwidth issues or that others are using GenAI tools to keep up.
The university collects vast amounts of data—from surveys, platform usage, and third-party EdTech tools—but there’s little transparency about how this data is used. Educators like Dr. Evans are expected to reflect on this data in their annual reviews, but they receive minimal training in data interpretation or AI literacy.
Discussion Points:
General Reflections:
- What are the visible and invisible ways data and AI shape the student experience?
- How do institutional practices around data and GenAI affect trust, transparency, and equity?
Linked to LO1 (Critically assess key contemporary debates…):
- How does this scenario reflect broader debates about surveillance, data ethics, and student agency in higher education?
- What are the implications of GenAI use being hidden or stigmatized?
Linked to LO2 (Apply specific learning theories…):
- How might constructivist or connectivist theories support more open and ethical use of GenAI in learning?
- In what ways could experiential learning help students and educators develop AI and data literacy?
Linked to LO3 (Critically reflect on digital technologies and practices…):
- How can educators critically reflect on their own use of data and AI tools in teaching and assessment?
- What are the risks of relying on incomplete or misunderstood data to evaluate student engagement and success?
- How might institutions better support transparent, inclusive, and ethical digital practices?
