A Fictional Science, Learning, and Evaluation Future Fest
Sarah M. Dunifon
A space shuttle on display in an airplane hangar.
As an informal STEM learning evaluator, I often think in patterns and context. This has led me to be a bit of a futurist, or someone who imagines and attempts to prepare for different possible futures. Lately, I’ve been inspired by the MIT Future Fest, an upcoming and inaugural convening that invites participants to explore the future through diverse topical lenses. I’ve also been an ardent follower of the Center for the Future of Museums, an initiative from the American Alliance of Museums that seeks to imagine the future of the industry. Their TrendsWatch Report is always a great read and exercise for folks exploring what potential futures could mean for our field.
This got me thinking about what my own “Future Fest” might look like. So, I put together an imaginary “Science, Learning, and Evaluation Future Fest” highlighting the topics and questions I think are the biggest areas of focus in the current moment as we look toward the future. Many of these ideas come from conversations with others in the science and public engagement space, or with other evaluators. Please enjoy a read through our fictional “program” and let me know what you think!
Session 1: The Place for AI in Informal Learning
Much attention has been paid to AI in formal education, and there are certainly inherent pros and cons to implementing these tools in the classroom. Less attention has been given to informal learning spaces and how they might engage with AI tools in the learning process. While cultural institutions may implement AI in their administrative practices, or even design exhibits that incorporate AI technologies, the learning process in informal spaces must be approached with a more critical lens as informal learning is often more inquiry-driven, hands-on, and experiential.
Learning is a social and relational process, and learning with others often has an outsized benefit to learning alone. Experts have stated that the use of AI as a supplemental tool could be quite useful to students seeking personalized, immediate instruction, or to those looking into niche topics outside the standard curriculum. But we must ensure that it is viewed as a supplemental tool, not a way to supplant standard instruction and remove this social/relational aspect.
In informal learning spaces, there is a certain magic to the hands-on, place-based, and applied learning opportunities offered to people of all ages. Here, we must explore if AI technologies will actually enhance these learning experiences and offer new ways of engaging with content, or if - as is the potential danger in the classroom - they will replicate and supplant the learning experiences designed to be exploratory, hands-on, and in-person. There may be creative applications of AI in informal learning spaces, and investigations into what these applications are that improve learning experiences is an interesting area to watch.
Session 2: Evaluators as Relational Ambassadors to Data and Knowledge
Evaluation has historically been seen as a relationship between an outside expert and the program managers who run an initiative, where the focus of the work is often extractive and non-inclusive of the initiative’s participants. In more recent decades, work has been done to encourage a more relational approach to evaluation, where ongoing partnerships are developed, participants are included as decision-makers, and evaluators amplify the inherent expertise of program leaders and participants, rather than their outside opinions.
As organizations explore AI and other machine learning options to save time and expenses, it is critical that we do not lose the decades of work that has been done to develop our relational practices. The future of evaluation will certainly employ some new technologies (e.g., AI transcription tools), but there is an inherent danger in rushing into new practices without interrogating the costs. It’s widely known, for example, that AI technologies have been trained on publicly available data sets on the Internet. Data ethicists have made it clear that these training sets are not free of bias and may in fact perpetuate inequity and discriminatory practices.
Evaluators are still a crucial part of the measurement and learning ecosystem. Consider what welcoming AI tools to analyze participant data may do, and note their inherent biases and inability to recognize and repair these biases. An AI tool’s positionality statement simply does not exist. There is a human touch needed to set priorities, negotiate interests, design for and engage individuals in data collection, and ensure participants are a robust part of the interpretation and implementation of findings. A question we might ask ourselves is, “Where must we preserve the human touch in social science research and evaluation?”
Session 3: Embedding Climate/Environmental Education into All STEM Fields
With the climate crisis impacting us more visibly and on a more regular basis, it’s past time we think about how to embed the critical messages of climate and environmental education into all STEM learning. Doing so is critical to the health of our climate and ecosystems, and can root all other STEM learning experiences in real-world context.
This approach gives us not only an opportunity to practice cross-disciplinary skills, but also works towards a future in which meaningful action is taken to support our climate and environment in all facets of our lives, from engineers selecting and designing for more sustainable materials to medical researchers and/or policymakers investigating and planning for the different impacts climate change will have on populations around the globe.
Educators in informal learning spaces have the ability to stretch beyond a standard curriculum that many formal educators are beholden to, and to instill these integrated and imperative practices in our programs. Ultimately, increasing knowledge and changing behavior in our visitors and participants is one more tool in our arsenal as climate protectors.
Session 4: Meaning-Making as a Participatory and Active Practice
The formulation of knowledge and meaning is an inherently participatory and social action in research and evaluation. People have different values, histories, and interpretations of experiences. Those who practice participatory methods know that including more voices leads to a fuller, more diverse, and more accurate understanding of findings and data.
Meaning-making in the broader world can be seen as an active and changing process; one in which the nature of knowledge creation and the idea of the “expert” has changed. Many scholars and practitioners have recognized that the way information is shared has shifted in light of new technologies, like social media and short-form video, and that the ability to attract attention to your message can matter more than formal credentials or experience.
Another way that meaning-making is shifting is through how users engage with short-form video. Events (for example) are no longer simply experienced, but documented as well. And the way that they are documented, packaged, and presented to others speaks volumes about what was important to attendees, their values, and what they felt was worth sharing with others. This in and of itself is a form of meaning-making, and a rather under-explored one for cultural institutions and nonprofits, in my opinion.
And so, we must ask ourselves, “What does all of this mean for the ways that we share information, construct meaning, and conduct evaluations with our participants?” We must be active in our understanding of how meaning is constructed and conveyed to others in our changing world.
Session 5: The Future of the Museums and Nonprofits Workforce
Several current trends are influencing the museums and nonprofits workforce, from the demographic cliff impacting universities to economic strain preventing professionals from accessing advanced degrees. Disinvestment in science and education at the federal level has also deeply impacted the field, providing fewer opportunities for professional development of current employees, hiring and retention of employees, and the ability to contract with professional service providers.
One potential shift to account for these changes might be towards professional credentialing and away from formal educational degrees at higher institutions. This type of credentialing - sometimes provided as certificate programs or courses from expert institutions - can be more affordable to students.
Professional societies are also working to determine the skills, knowledge, and dispositions needed to succeed in these fields. Visitor Studies Association, for example, is working on refreshing its existing evaluator competencies framework through the REFEDINE Visitor Studies project. The Association of Science and Technology Centers has a Professional Pathways in Informal STEM Learning framework to help professionals advance. Perhaps we will see more of this, led by professional service organizations, professional societies, and unique university efforts working to fill the gap that professional degrees may leave.
Other considerations are economic strain and stratification that may lead to fewer people able to take lower-paying jobs in the science and culture sector, as well as the incorporation of AI into these spaces, which may shrink the number of open roles, particularly for incoming professionals.
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Personally, I find it helpful to prepare for different futures and to explore and imagine how informal learning and public engagement with science may shift over time. I hope you enjoyed a peek into my brain and what I’m chatting with people about these days.
I’d love to hear from you - please let us know at hello@improvedinsights.com what you’re thinking about these days. If you need a strategy partner to help you design for the future of learning and evaluation, we’d be happy to chat.
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