Critical AI Engagement Framework, Version 1.0

This framework draws on Hosseini's (2026) forthcoming paper in The Geographical Journal, which examines how generative AI tools reproduce racial, gendered, and class-based representations through algorithmic coloniality (Mohamed et al., 2020). That work demonstrates how seemingly neutral prompts encode dominant cultural assumptions, producing outputs that reflect and reinforce existing inequalities.

The Critical AI Engagement Framework extends this analysis into a practical pedagogical tool, mapping how individuals engage with generative AI across two axes: epistemic posture and structural consciousness. It is designed for use across educational contexts, supporting educators and learners in moving beyond prompt refinement toward critical, collective, and structurally informed engagement with AI systems.

Critical AI Engagement Framework — Hosseini
Critical AI Engagement Framework
Grounded in Mohamed et al. (2020), Benjamin (2019), Noble (2018), Zembylas (2023), Crenshaw (1991)
Hosseini — Version 1.0, March 2026
For academic and workshop use
Not for citation without permission
With special thanks to Dr Sara Camacho Felix

Generative AI tools are now embedded in higher education, used to draft text, generate images, produce code, and synthesise research. Yet these tools are not neutral. They encode the assumptions, hierarchies, and exclusions of the data they were trained on, reproducing patterns of racial, gendered, and class-based harm even as their outputs become more sophisticated (Benjamin, 2019; Mohamed et al., 2020; Noble, 2018). Understanding how students engage with these tools, and what that engagement does or does not interrogate, is therefore an urgent task for educators.

The Critical AI Engagement Framework maps educational engagement with generative AI across two axes. The horizontal axis describes an individual’s epistemic posture toward AI: from treating its outputs as authoritative, through questioning them, to recognising the colonial and structural conditions that shape what AI knows, whose knowledge it centres, and what it silences (Hosseini, 2026; Maalsen, 2023). The vertical axis describes how far an individual recognises AI as a socio-technical artefact shaped by relations of power, rather than simply a tool with fixable flaws (Benjamin, 2019; Quijano, 2000; Zembylas, 2023). The positions individuals occupy are not freely chosen but reflect the institutional, curricular, and social conditions that shape how they learn and work. The framework’s aspiration is not a more capable individual user but collective action: sustained, community-grounded engagement that works toward structural change (Camacho Felix, 2025; Mohamed et al., 2020).

Epistemic deference
AI as neutral oracle
Critical interrogation
Outputs questioned
Epistemic agency
Whose knowledge?
Collective / relational
Critique with others
Individualized
engagement
AI as personal tool,
neutral & apolitical
The Uncritical Receiver
Accepts outputs; AI naturalized as neutral knowledge source
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): student treats AI outputs as objective, without recognizing that GenAI encodes "opaque, inconsistent cultural assumptions" shaped by historically racist and sexist training data (Keshishi & Hosseini, 2023; Benjamin, 2019).
For educators
Make the assumption of neutrality visible through practical experiments. Ask: whose perspective does a GenAI output reflect by default — in an image, a summary, a clinical recommendation, or a generated essay? Drawing on Day & Esson (2025) and Hosseini (2026), show how seemingly neutral prompts reproduce skewed cultural defaults across output types.
For students
The student experiences AI outputs as "natural" rather than constructed. As Benjamin (2019) argues, socio-technical artefacts are not static reflections — they are shaped by the feedback and values of those who built them. Students need a framework to see this, not just permission to question.
The Cautious Pragmatist
Checks outputs; AI still framed as neutral instrument
Theoretical anchor
The student audits outputs for factual errors but not for the cultural assumptions encoded in them. As Day & Esson (2025) show, even anomalous outputs require users to "remain vigilant to the opaque and shifting nature of generative AI tools" — vigilance the Cautious Pragmatist applies technically but not epistemically.
For educators
Shift from "is this accurate?" to "whose accuracy?" Introduce temporal dynamism (Kleinman, 2024): improved outputs — whether images, text, or code — do not mean underlying biases have been addressed. Use Spennemann & Oddone's (2025) technique of asking GenAI to explain its own outputs as a critical exercise.
For students
May believe that better prompting solves the problem. However, prompt refinement "would not address the underlying biases within the datasets themselves" (Hosseini, 2026). The student needs to move from refining inputs to interrogating the training data and the colonial logics embedded within it.
The Epistemically Alert
Interrogates whose knowledge is centered; notices silences
Theoretical anchor
Algorithmic coloniality (Mohamed et al., 2020): student recognizes that AI systems embed a "dominant, Eurocentric worldview" that upholds hierarchical, racialized, and gendered ways of knowing. Connects to Noble's (2018) algorithms of oppression and Maalsen's (2023) algorithmic epistemologies.
For educators
Move from naming bias to interrogating its origin. Use Quijano's (2000) coloniality of power to show that AI's racial and gender defaults are not errors but expressions of colonial hierarchies embedded in training data. Ask: what would an AI trained on non-Eurocentric datasets produce differently?
For students
May feel isolated, especially when institutional AI guidance frames the issue as a technical problem. Wilby & Esson's (2024) call for "capabilities, caveats, and criticality" provides legitimizing language. Connect to communities of practice doing this work.
The Isolated Disruptor
Critiques AI alone; change without solidarity
Theoretical anchor
Individual critique of algorithmic coloniality, however sophisticated, cannot address structural problems in proprietary and inaccessible training datasets (Amoore et al., 2024). Mohamed et al. (2020) are explicit: structural change requires "political coalitions and communities," not individual actors.
For educators
Connect students to collective and cross-disciplinary action. Addressing algorithmic coloniality requires breaking down "disciplinary and departmental silos" (Hosseini, 2026; Maalsen, 2023). Individual insight without structural leverage changes nothing about the datasets or systems producing harmful outputs.
For students
Risk of cynicism or disengagement when individual critique runs up against inaccessible, proprietary datasets and opaque systems. As Hosseini (2026) demonstrates, surface improvements in GenAI outputs can mask rather than resolve the underlying colonial logics — students need community and strategy, not just analysis.
Partial structural
awareness
Senses bias or harm,
lacks systemic account
The Uneasy Adapter
Senses something wrong; lacks language to name it
Theoretical anchor
Pre-conceptual awareness of algorithmic harm: student senses that something is "off" in AI outputs — perhaps noticing racial or gender skew — but has not yet encountered the theoretical vocabulary to name it. This is the moment described by Day & Esson (2025) when outputs produce "surprising results."
For educators
This is a threshold moment. Offer concepts — algorithmic coloniality (Mohamed et al., 2020), algorithms of oppression (Noble, 2018), socio-technical artefacts (Benjamin, 2019) — as language for what is already felt. Hosseini's (2026) method of prompting GenAI and critically analyzing outputs is a replicable pedagogical entry point adaptable across text, image, and code generation.
For students
High potential. Already doing affective critical work. Avoid rushing to resolution — the unease is epistemically productive. GenAI outputs should be approached "not [as] surprising, but as symptomatic of racialised and gendered logics" (Hosseini, 2026) embedded in training data across all output modalities.
The Informed Skeptic
Identifies bias in outputs; most common profile
Theoretical anchor
Can identify racial and gender skew in outputs — consistent with quantitative evidence (Cheong et al., 2024; Currie et al., 2024, 2025) — but frames it as a dataset problem rather than an expression of algorithmic coloniality (Mohamed et al., 2020). The systemic account is absent.
For educators
Move from "bias as glitch" to "bias as design." Use Benjamin's (2019, p. 59) argument that training datasets carry "the prejudices of the individuals who compiled them." Ask: why does a GenAI default encode particular assumptions about race, class, gender, or expertise — whether producing an image, drafting a clinical summary, or generating a curriculum resource?
For students
May believe that surface improvements — more realistic outputs, more diverse teams, better prompts — will resolve the issue. Hosseini (2026) demonstrates directly that successive GenAI model versions produced aesthetically improved outputs while reproducing the same racial and gendered logic. The technical fix does not address colonial logics in the training data.
The Structural Analyst
Names AI harms systemically; connects to power
Theoretical anchor
Understands AI as a socio-technical artefact (Benjamin, 2019) shaped by Silicon Valley's role as "part of the United States, a global hegemon and a successor to European colonial powers" (Keshishi & Hosseini, 2023). Connects algorithmic coloniality (Mohamed et al., 2020) to concrete outputs.
For educators
Deepen from analysis to action. Introduce reparative description (Parry, 2023): how might geographers work with public image repositories to revise false past categorizations? Introduce Zembylas's (2023) strategies for "undoing the ethics of digital neocolonialism."
For students
May become frustrated that structural analysis does not translate into change. Channel into cross-disciplinary collaboration. Addressing problematic training data requires collective action and "relational approaches that emphasise the spatial and political contexts of algorithms" (Maalsen, 2023; Hosseini, 2026). Analysis without community and outlet risks paralysis.
The Emerging Ally
Seeks solidarity; building shared critical vocabulary
Theoretical anchor
Transitional position between individual and collective consciousness (Freire). Recognises that critique must be collective but lacks the structural analysis to ground it yet.
For educators
Facilitate cross-disciplinary collaboration explicitly. Addressing algorithmic harm requires breaking "disciplinary and departmental silos" (Hosseini, 2026; Maalsen, 2023) — across education, geography, data science, and activism. Connect emerging allies to existing coalitions and communities of practice doing this work.
For students
Motivated by justice but may lack the analytical vocabulary to sustain critique under institutional pressure. Pairing with theoretically grounded peers — including those with lived experience of the harms being analyzed (cf. acknowledgments in Keshishi & Hosseini, 2023) — is more generative than educator-only support.
Structural
consciousness
AI as site of
coloniality & harm
Conscientized but Constrained
Sees the system; defers under institutional pressure
Theoretical anchor
Understands algorithmic coloniality and its harms but operates in institutional systems — curriculum, assessment, professional bodies — that have not caught up with the critique. Within many national contexts "there are nascent discussions on the ethical issues of using Gen AI technologies within tertiary education" (Hosseini, 2026) — the institutional conversation is beginning but remains uneven.
For educators
Name the institutional lag explicitly. Developing "algorithmic literacy as part of wider digital literacy initiatives" (Kong et al., 2023; Zembylas, 2023; Hosseini, 2026) is a growing expectation — the conversation is beginning, and students can actively contribute to shaping it rather than waiting for institutions to catch up.
For students
Risk of internalizing structural constraint as personal inadequacy. The student's tension is not a sign of failure — it is evidence of structural contradictions that institutions have not yet resolved. Validate the critique while building pathways to act within and against institutional constraints.
The Critical Refuser
Refuses metaphorical framing; acts on structural critique
Theoretical anchor
Tuck & Yang (2012): decolonization is not a metaphor. Student refuses cosmetic diversity framings and demands structural change to what AI produces and whom it serves.
For educators
Support with Mohamed et al.'s (2020) practical recommendations: identifying sites of coloniality in AI systems, understanding where and how algorithms are made, engaging in reparative description (Parry, 2023), and developing local and national policy challenges to colonial algorithmic logics.
For students
May encounter resistance from colleagues who frame AI critique as technophobia or obstructionism. Documentation and publication — as Hosseini (2026) demonstrates — transforms resistant practice into sharable pedagogical resource. Connect to communities doing this work across disciplines; the argument gains force collectively.
The Critical Collaborator
Challenges AI's epistemic order; builds alternatives
Theoretical anchor
Actively participates in co-creating "instructional materials that transcend boundaries" (Hosseini, 2026) — resources that make algorithmic coloniality visible and addressable across GenAI modalities. Draws on intersectionality (Crenshaw, 1991; Hill Collins, 2019) to hold race, gender, and class in simultaneous analysis rather than treating each as a separate problem.
For educators
Commission rather than assess. Meaningful critique of algorithmic coloniality requires centering those with lived expertise in the harms being analyzed — not as informants but as co-authors (Hosseini, 2026). This student's contribution should shape pedagogy, not merely illustrate it. Invite co-authorship, co-design, and co-delivery.
For students
Risk of co-option — being absorbed as institutional evidence of diversity without structural change. Hosseini's (2026) reflexive positioning — centering colleagues with lived expertise in racial and gender inequity — models how genuine co-production differs from performative consultation. Support students to name and resist this distinction.
The Praxis Collective aspirational*
Reflection + action with others; pluriversal praxis
Theoretical anchor
Camacho Felix's (2025) decolonial imaginations and collective imagination — "unveiling different possibilities for addressing injustices" through relational, mutual aid. Mohamed et al.'s (2020) political coalitions. Benjamin's (2019) abolitionist tools for dismantling the New Jim Code in AI systems.
For educators
Collective praxis around GenAI requires institutional conditions: time, resource, partnership, and willingness to redistribute epistemic authority. It demands cross-disciplinary collaboration, reparative dataset work (Parry, 2023), and policy advocacy (Hosseini, 2026; Mohamed et al., 2020) — none of which individual pedagogy alone can produce. Educators must build the structures, not just model the position.
For students
Students here are co-researchers and co-educators. Hosseini (2026) models this directly: conducting experiments, publishing findings, and encouraging readers to replicate and extend the work with a critical eye. Sustain rather than assess — the goal is ongoing collective action that outlasts the course, not a demonstration of competence for a grade.
← epistemic deference
collective / relational agency →
Movement across these axes is non-linear — students may hold multiple positions simultaneously across different contexts and knowledge domains
Theoretical grounding
Horizontal axis: Mohamed et al. (2020) — algorithmic coloniality; Noble (2018) — algorithms of oppression; Maalsen (2023) — algorithmic epistemologies and situated knowledge  ·  Vertical axis: Benjamin (2019) — socio-technical artefacts encoding racial inequity; Zembylas (2023) — decolonial AI in HE; Quijano (2000) — coloniality of power; Camacho Felix (2025) — decolonial imaginations and collective action
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Holding virtual office hours - suggestions

A colleague asked about holding virtual office hours. The question entailed both how to do this in terms of the pedagogy and the technology. So, I drafted a document that addresses some suggestions around holding virtual office hours (and tutorials) which can be found here: https://sway.office.com/yqhBJFryvfm46a5e?ref=Link as a Sway document that can be printed and shared.

A colleague asked about holding virtual office hours. The question entailed both how to do this in terms of the pedagogy and the technology. There were quite a few considerations to take in to account namely:

  • class and cohort size;
  • possible apps/tools to be used;
  • getting the most out of a virtual session;
  • preparation required prior to a virtual office hour/tutorial;
  • expectations for students/attendees of said events;
  • and many more!

So, I drafted a document that addresses some suggestions around holding virtual office hours (and tutorials) which can be found here: https://sway.office.com/yqhBJFryvfm46a5e?ref=Link as a Sway document that can be printed and shared.

I should note that, while I focus on using Microsoft Teams, the principles for this apply to Zoom and Google Hangouts, among others. The main differences are that Zoom allows 'breakout rooms'. I haven't used Google Hangouts for a similar purpose, so can't comment.

I hope this is helpful!

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Getting students to use (new) apps

I've decided to quickly write up some thoughts on getting students to use new apps for learning and teaching as a reflection on what I've observed over the last few years and more recently.

It's safe to say that I approach this post from the point of view that there are many opportunities for digital education to enhance the learning and teaching experience.

More specifically, I'm writing this short article in relation to #MicrosoftTeams and what you need to do to ensure successful uptake by students and staff. What I write here applies to any other new systems - even ones such as Moodle.

Social media all around

It's fair to say that a lot of students and even staff in higher education use a variety of social media for various purposes. Students and staff still may use Facebook to connect with their friends and family, and classmates and course mates. Statista has a wealth of data on users of Facebook and Twitter, if you're interested.

Both of these groups may, if they're interested, use Instagram to create, collate and share images and/or video - photography and multimedia generally. A good number of students use Snapchat and in the UK high numbers of users aged 18-24 are likely to use Snapchat. Some university staff even use Snapchat to engage students in the classroom - with success!

Students aren't digital natives

A lot of my colleagues in higher education might understandably believe that because students regularly use apps like Snapchat, Instagram, WeChat, Facebook and others that this ability translates into a being able to effectively use digital tools and being tech savvy - being digital natives. - well beyond what my colleagues may have grown up with.

A lot of us use technology to 'passively soak up information' which could be scrolling through a Facebook or Instagram feed and reacting to posts. Yes, perhaps we share the odd image, video or article and add a bit of commentary - commentary - but are these acts critical or rather habitual?

I'd say these are habitual acts that form part of a series of daily routines in which users might fill time - gaps between spurts of attention to other things - and/or while navigating and exploring the vast ocean of information that's out there. From funny memes to noteworthy articles or click-bait news - it's all information, and it doesn't take much effort to open our favorite app to access that information! And this leads me to my main point...

New and unfamiliar systems

In a university setting, students will often use platforms such as Moodle, Blackboard, Google Apps for Education or similar. Microsoft has an answer, too, #MicrosoftTeams. All of these platforms offer a range of activities, structures and systems that can greatly help to manage the design, flow and presentation of information for users.

One thing we should not forget is that the aforementioned systems are created for the purposes of education, business and collaboration generally which go beyond the basic functions of Facebook, Snapchat and Instagram which are primarily for 1:1 or small group chats/discussions that are often centered around the sharing of media.

However, what unites all of these systems, platforms and apps for education is that generally these are unfamiliar to students unless there is a chance that they'd previously used one of them in school. Even then, if, for example, students have used Moodle in school, the look and feel of the system may not represent what they end up seeing in a university setting. Indeed, where modules on Moodle are still often used as repositories rather than engaging learning and teaching hubs, this can be daunting for users of 21st Century systems such as Google Search or Bing that offer information at your fingertips with few hurdles if you understand how to do key word searches. This leads me to a question:

How often do you explicitly train your students in using your university system or an app for a module?

I suspect not a lot of programmes take the time to explicitly provide training to students. That said, think of all the time we spend when we start a new post to receive training on the following:

  • health & safety
  • diversity
  • data protection & GDPR

So why don't we spend a bit of time investing in the training of digital abilities and skills rather than assuming that the use of a smart phone = being digital and tech savvy? Taking a selfie does not make you a tech expert!

New systems require explicit training

#MicrosoftTeams is taking off as the latest app for learning, teaching and collaboration generally within higher education in the UK. Indeed, I'm using it on a module that I lead on and it's confirmed a few things that I learned a few years ago.

Between about 2014-2016, I was working with pre-sessional student who would come to the UK during the summertime period to study English as a foreign language for the purposes of improving their academic English language abilities. Students generally had an English language knowledge of about B1 to B2 and they had digital skills that ranged significantly. Nearly all had a smart phone and could use the main apps of the day.

We used Moodle as our online platform with our students to set readings, have online discussions and set assignments that students would write up, upload and submit. Moodle was a system most students hadn't used and would only use in their university studies. In order to ensure the students' success in using the online platform as an enabler rather than a distraction, I convinced colleagues to allow all students to receive 1 hour of explicit instruction on hows and whys of using Moodle.

During the summer, we had around 700 students over 3 cohorts that we needed to train up. So, we booked computer labs and trained students in groups of 30-50 each in the space of about 1 hour; there were frequently 3 staff (including myself) on hand to help out and ensure that everyone was on the same page.

Effective training = tangible benefits

Although with the sheer numbers of students to train some days were long, the result was that we were able to ensure that over 90-95% of the students understood what Moodle was and what it was for, why we were using it and how they could access it. This number was able to ensure that we had created a relatively strong community of learning in which students could support each other in understanding and practicing how to navigate an unfamiliar and new system, which in this case was Moodle.

As a side benefit, also important, for students whose first language wasn't English, they were able to understand that they were going to get a lot of writing practice in English, which would boost their confidence in writing more fluently (albeit not always accurately) in a relatively authentic, meaningful way that they could then transfer back into their own writing for essays and assignments.

Key takeaways

The key takeaway here is this: If we throw the apps at students, they don't always get it. They generally get Instagram, Facebook, Snapchat... because those are fun apps for fun, social stuff. They won't necessarily get apps for education, business and collaboration though; these aren't natural apps - they aren't always fun (or associated with fun!), so we should prepare our students first before letting the apps loose!

With nominal training (1 hour) students will:

  • develop a critical awareness of the reasons for using the system;
  • gain effective practice in using the basic, required elements of the new system;
  • develop transferable digital skills that can be used for approaching and understanding new systems.

So, if you're going to teach on a module that involves Moodle, Microsoft Teams and/or similar, and/or if you have a student induction coming up, take the time to build in 1 hour of training.

The results will pay off and speak for themselves!

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Education Education

Digital, information and media literacies, and the development of related abilities and skills of students and staff

An image of two mics, a desk and chair representing a recording studio. Source: Unsplash

I have been able to gain a wealth of understanding of the varying levels of digital, information and media literacies among the students and staff through my role of leading on the module and seeing the module launch. What I seek to write about here relates to the digital and information literacies of both groups of stakeholders from my own interactions and observations.

Part of MNGT160 is particularly innovative in that we are doing the following. To allay doubt, in our use of 'innovative' I'm talking about breaking with tradition within our own context rather than creating the next best thing since sliced bread...

  • we have created a podcast mini-series, of sorts, as the central medium for the content; all episodes are transcribed as standard, and hyperlinks to further relevant texts and media are added in to make the transcripts be a bit more engaging, insightful and useful as a potential resource for those who might want to read along as they listen and/or read the transcript generally;

  • the module radically departs from traditional modes of delivery such as having solely face-to-face sessions and using Moodle as a static repository of resources;

  • this module is run entirely online via Microsoft Teams and, to a lesser extent, OneNote is used to collect and collate further references, articles and other supplementary content;

  • there are a few face-to-face events that directly tie in with the overarching topics and sub-topics.

The inspiration for doing the podcasts came from colleagues agreeing to try out podcasting as the main content vehicle and my own listening to Sophie Bailey's #EdtechPodcast and a presentation by Nellie El Enany on using podcasts in the classroom at ELTME2019.

Media literacies

Media literacy is an umbrella to consider other literacies, including news literacy, visual literacy, information literacy, technology and platform literacy, and data literacy.

Media Literacy and Politics of Identity - Resources for Educators. https://criticalmediaproject.org/media-literacies/

The first point I'll touch on has to with the idea of using podcasts as a central medium. Initially, my colleagues and myself all felt this would be a particularly innovative, flexible and different way for students to engage with the different topics and subtopics of the module.

The benefits in terms of flexibility for students seemed numerous and too attractive to pass up: students could listen to the episodes of the podcast anytime, anywhere, whether this was while on the bus, in the gym, while cooking and/or while reading or studying. In contrast to a recorded video lecture, a podcast seemed to make more sense for this particular module.

Generally, we felt fairly confident in using this as the medium for the module. However, as further conversations were had, I quickly came to understand a few things:

  • not everyone listens to podcasts;

  • not everyone knows what a podcast is and/or entails;

  • not everyone listens to 'talk radio' such as BBC Radio 4 in the UK or NPR in the US;

  • not everyone can imagine what a podcast is if they don't listen to the radio, let alone talk radio!!

What I was perhaps struck by most was that some colleagues had difficulty imagining what a podcast might entail, whether we're talking about a podcast itself as a concept, or in another case, actually sitting down to record a podcast. This general lack of media literacy in itself created a few issues, one which relates directly to how the time of colleagues are allocated via the workload.

However, this also presented unique opportunities for learning, staff development and creating a positive impact upon colleagues who had not previously engaged with and/or used podcasts or podcasting previously.

A practical issue: workloading

In terms of workload and the infamous 'workload model', the idea of preparing for a podcast nearly became a bigger issue than it was. Most podcasts are either informal talks about a topic or more or fully-informed chats, debates and/or discussions about a topic.

However, from what I observed, part of the lack of understanding more generally of what a podcast often is and can be did lead to some colleagues believing that they would have to spend hours and hours preparing for a podcast. Some colleagues initially thought preparing for a podcast would be similar to how a lecturer might spend time writing up a lecture and/or creating or repurposing slides, all of which can take a significant amount of time if the topic to be covered is new and/or fresh.

Such lengthy preparation might also be merited if we planned for the podcasts to be a debate rather than a discussion in which colleagues are talking about a question from their own subject lens perspective. If we were planning on a talk where we wanted to 'catch people out', then sure, preparation is key! However, in the cast of our own podcast series for MNGT160, I wanted people to do a bit of preparing (1 hour or so maximum) and come to the recording studio to sit down and have a collegial, informed chat about the topic in question.

Butterflies & speaking to mic

Recording podcasts, like recording video or taking a photograph of someone, is likely to generate a certain level of nervousness even with colleagues who may have daily student-facing roles whether these are administrative or teaching focused. It's easy to assume that those who are confident in their daily roles will be confident speaking for a podcast. Nope, this isn't always the case!

That said, I found the easiest way to manage the nerves of speakers, for the most part, was by getting people to meet for about 15-20 minutes, chat about what we'd generally aim to chat about and briefly sketch out tentative talking points for each episode, bearing in mind that we could be flexible as long as we focused on the general question for each episode and spoke no more than the allotted amount of time.

Another way that I found that worked particularly well for both myself as a new podcast/radio host and for other colleagues was to get in touch with the press office on campus. One colleague, Paul T, had extensive prior experience as a journalist and so was particularly helpful in coaching and mentoring colleagues and myself in terms of how to speak in a radio-type setting, what to do and how to approach things generally. Our digital media engineer, Martin T, was also particularly helpful from the recording, technical and design side of getting a podcast up and running. Sure, we could have recorded the videos with a mobile phone, but we wanted to get things right the first time around.

On reflection...

From a leader's perspective, I perhaps should I have predicted this lack of understanding of the notion of podcasts, podcasting and talk radio generally. I was very enthusiastic and wrongly assumed that people generally listen to spoken word radio shows, if not podcasts.

But then again, going back to my earlier point, not everyone understood the concept of a podcast and they instead immediately relied upon what they did know: teaching and lecturing which are very different (!) from spoken word for a radio show or a podcast.

Going forward, when podcasts are going to be the main content vehicle, I'll take the time to do a bit of fishing of my colleagues to see whether they listen to the radio or podcasts, and if not explain what a podcast is and share one of the podcast episodes we've since created. Even if 3-4 colleagues out of the 7 know what a podcast is, it's best to spend time making sure the other 3-4 are fully aware so that they can be more relaxed when taking part.

Yes, of course, I could have shared a currently running podcast with a colleagues, though the issue would have been 1) choosing the 'right' podcast and 2) ensuring that everyone had a list (even briefly) to a few moments of the podcast - something that I can request, but not demand and 3) potentially setting unintended expectations per quality, length and so on!

The key takeaways

Doing a podcast

Whereas a lecture is designed to tell a story or impart information, often presented by one person from start to finish with relatively fixed starting and end points within a specified timeframe, a podcast should be a dialogue, a conversation between the speakers present. This is something that one can prepare for but one cannot entirely script a podcast episode else it might end up sounding unnatural. There is some preparation on the part of the host and speakers.

However, the good news for anyone wanting to try out podcasting for learning, teaching and development is that the time involved is far less than the amount of preparation that goes into a traditional face-to-face lecture that may consist of writing up a script, creating/modifying a series of slides and rehearsing a lecture. While a basic level of pre-recording preparation required can consist of bullet points and a brief meeting either face-to-face or a couple of email exchanges to lay the groundwork for a good, fruitful conversation, as I noted above, it is not necessary to plan out the entire episode of an informed conversation.

Media literacies

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