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I have had lots of rich conversations with colleagues about our scholarly systems and practices, including peer-review. Kumashiro et al’s Thinking Collaboratively about the Peer-Review Process for Journal-Article Publication greatly influenced my thinking and practice in this area. Conversations around peer-review can be expansive, and some of the ones I’ve had over the years explore how to prepare early career colleagues to give “good” feedback. The last time I taught a doctoral research seminar (which I am teaching again in the Fall), I involved my students in an authentic peer-review experience, which then led to a co-authored paper on the experience of peer review. We published this paper in Postdigital Science and Education, and I’m including the abstract and link to the full paper below.

While academic peer review is the cornerstone of scholarly publishing, it presents significant challenges for early-career researchers. This collective paper describes an authentic peer review experience within a doctoral seminar. In collaboration with Postdigital Science and Education, students acted as peer reviewers for manuscripts under consideration, wrote individual reflections on the process, and engaged in group discussions to analyze their experiences. Students grappled with questions of expertise, the challenge of providing constructive criticism, the ‘hidden curriculum’ of academic publishing, and the need for clearer reviewer guidelines. These insights are then discussed and expanded by seasoned scholars in the field, with the goal of a transparent and iterative dialogue that strengthens scholarly communities.

Veletsianos, G., Arabadzhy, G., Athilat, V., Bartucz, J., Dogra, S., Fredrickson, N. R., Goeke, M., Jeon, S., Urena, M., Jandrić, P., Green, B. J., & Jackson, L. (in press). Postdigital peer review: A vulnerable space between freedom and responsibility. Postdigital Science and Education. https://doi.org/10.1007/s42438-025-00604-6

The Washington Post published an article, summarized as follows: “Some online colleges allow students to take unlimited courses on their own time, leading to quick degrees and worries about devaluing credentials.” The piece focused on the phenomenon of degree hacking, in which learners have figured out that certain credentials can be attained through some combination of (1) prior learning assessment and recognition (PLAR), (2) competency-based assessment, (3) flat tuition independent of course load, and (4) self-paced online courses. Together, these features allow what critics call degree hacking: the idea that a program’s design can be exploited to earn credentials in a fraction of the expected time.

There is a great deal to unpack here, from questions about what counts as an “appropriate” time to complete a course or degree, to distinctions between forms of online education, to questions about who has access to higher education and who does not. But the issue I wanted to address was our tendency to police this phenomenon rather than approach it with curiosity. So, I wrote the letter below, which the Post published last week. I am posting it here for posterity.

As a professor of education and researcher of online learning, I was disappointed to see speedy graduates framed as a problem to be investigated and policed in the April 20 front-page article “Educators alarmed by students ‘speed running’ online degrees.” Look again at these students. Before one student enrolled, they spent two months racking up credits through web tutorials after work. Another student finished 16 courses in 22 days while balancing full-time employment and a 6-year-old.

These learners aren’t gaming a loophole. They’re demonstrating self-direction, time management and strategic planning. These are the very skills higher-education leaders and employers claim to want from graduates. Whether a five-week philosophy course teaches as much philosophy as a 15-week one is a fair question. Whether these students are learning is not: They are demonstrating it on the assessments their own institutions designed to measure it. If the yardstick is wrong, that is an institutional problem, not a student one. And yet, as the article itself noted, the institutional response is to police them.

A few weeks ago, I was at the University of Texas at Arlington to lead deliver a keynote and two workshops. I am sharing short descriptions of these here for posterity, and as examples of the kinds of events that might be of interest to others.

Keynote: GenAI, Imagination, and Education Futures (60 minutes)

This keynote explores the promises, tensions, and challenges surrounding Generative AI in education, grounded in the history, research, and tensions around the use educational technology. The goal of this talk is to provide an open space for reflection, imagination, and strategic thinking about how we want to move forward and what we want to protect along the way.

Workshop 1: Creating Speculative Fiction to Envision Utopian AI Educational Futures (90 minutes)

This workshop engages faculty in writing short speculative fiction pieces that explore positive, utopian educational futures where AI is thoughtfully integrated to enhance human learning and connection.

Workshop 2: Navigating Possible Futures with Emerging technologies (90 minutes)

This workshop applies structured scenario planning techniques to help U of Texas faculty critically examine how their institution might look like in 2035 given current advances in AI and emerging technologies.

One of the concerns in our field of study is that persistent focus on things/technologies (e.g., mobile devices, virtual worlds, AI, online courses, etc etc) rather than problems (e.g., poverty, achievement, engagement, etc). The Journal of Computing and Higher Education has a special issue on “The Research We Need” in educational technology and Spencer Greenhalgh has an article in it that asks three important questions:

1. which problems should we solve?
2. who should solve those problems?
3. is solving problems always good?

It’s a thoughtful paper and well worth your time.

I’ve been receiving many, MANY, questions over the last year around what is and isn’t ethical when deciding whether and how to use AI. Two questions I’ve started asking myself are these:

• Would I be comfortable being on the receiving end of this?
• Would I want this for my loved ones, like my niece and nephew?

Call this the “Recipient Test.”

This simple question isn’t just about deciding whether to use AI or not; it also prompts us to consider how we use it responsibly and ethically.

Take the example of recommendation letters. Are you tempted to use AI to draft them? Imagine seeing a letter written for you and discovering it was generated by an algorithm that knows nothing about you. Worse, picture your child receiving such a letter at a pivotal moment. This makes us question the wholesale outsourcing of the task. However, the “Recipient Test” can also illuminate a more thoughtful approach, one shared by my brilliant friend and colleague Tonia A. Dousay. Imagine someone who writes a few of these letters a week. How might theυ use AI responsibly? They might provide the job description, the candidate’s key qualifications, and their own personal insights to a tool AI, then dedicate time to revising and personalizing the draft. In this way, the “Recipient Test” can lead us to use AI as an assistant rather than a replacement.

Consider grading and providing feedback – this is a treacherous terrain. Sure, it saves time, but I still remember truly personalized feedback that shaped my learning. Would an AI provide that? I also recall receiving generic feedback – not from an AI – that offered little value. Perhaps the “Recipient Test” here encourages us to think about how AI can augment, rather than fully automate, feedback, allowing educators to focus on providing individualized guidance. I’m suspect of the “saving us time” argument, but that’s a whole different post.

When designing learning activities, you might apply this test by asking: Would using AI to generate a learning activity create a memorable learning moment I’d like to participate in? Would it inspire curiosity in my niece? The “Recipient Test” might compel you to look beyond mere efficiency.

The use of AI in education – like the use of so many other technologies over the decades – is about thoughtful, measured, and critical integration. The test might lead us to conclude that AI is or isn’t appropriate in a particular case, but it also pushes us to define the parameters of its use to ensure a positive and ethical outcome for the recipient. By consistently asking this question, we are forced to pause, reflect on the impact, and make informed choices.

Aside: This kind of thoughtful consideration might be more challenging in cases where AI is embedded within tech/edtech products, where some of our agency is limited.

In 2020, a few months after the pandemic, I worked on a contract for the Commonwealth of Learning (COL) to create a course focused on online assessment. I taught the course three times before it transitioned into a decentralized model and became a standard part of the suite of professional learning opportunities that COL offers to partner institutions seeking to increase their capacity to offer online learning. Last I checked it was adopted by the University of Belize, Caribbean Maritime University, Ministry of Education and National Reconciliation at Saint Vincent and the Grenadines, Sam Sharpe Teachers College in Jamaica, Namibian College of Open and Distance Learning, and Cyril Potter College of Education in Guyana. As part of this effort, I wrote a book chapter describing lessons learned, and it’s now been published. Below is the abstract and citation, with links to the open access book:

Abstract

This chapter focuses on the design, development and lessons learned from the implementation of ten offerings of the Virtual University for Small States of the Commonwealth’s (VUSSC) Designing and Developing Online Assessments (DDOA) course. Designed for post-secondary teachers, instructors and educators, this online course was developed in the early months of the Covid-19 pandemic, and first delivered in September 2020, to help educators around the Commonwealth explore online assessment practices and principles. The self-paced course typically took six weeks to complete and required approximately five hours of independent work per week. It was ungraded and consisted of a range of learner activities including readings, podcasts, practical work, discussions and self-directed work. Its design focused on four principles: flexibility, trust, relevance, and localisation. Given its timing, it prioritised helping adult learners complete the course, or as many parts of it as they deemed relevant to their work. This chapter describes the four principles and explains how each one was reflected in the design of the course. It also describes how and why the course transitioned from a centralised version to a distributed model of offerings for educators.

Citation

Veletsianos, G. (2025). Professional Learning for Teachers and Educators across the Commonwealth: Designing Online Assessments. In Orange, B. et al. (Eds), Innovative Models and Practices in Teacher Development: Case Studies from the Commonwealth (pp. 112-122). Commonwealth of Learning. https://doi.org/10.56059/11599/5700

A couple of special issue CFPS came across my desk, so I thought I’d share them. Special issues are especially relevant and helpful to early-career TT faculty, as they might offer faster publication timelines, network building, and (potentially) lower competition :

• The International Journal of Designs for Learning (IJDL) calls for proposals for a Special Section on Generative Artificial Intelligence (GenAI) in Learning Design. We are seeking proposals for design cases involving GenAI technologies as not merely tools but as catalysts for change. In this Special Section, we are interested in publishing design cases that describe the usage of GenAI tools for developing artifacts, systems, or experiences for learning in diverse contexts. While the focus should be on the learner-facing outcomes of design, design cases should also describe the decision-making around the use of GenAI tools, explaining how learning designers balance the efficiency of these tools with the complexity of decision-making to ensure the ethical and responsible use of such technology.
• Educational Technology Research & Development invites proposals for replication studies in the fields of instructional design, learning sciences, and educational technologies.