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OPEN
EDUCATIONAL
PRACTICES FOR AI
IN EDUCATION
OPEN
RESEARCH
AI in
TEACHING
AND
LEARNING
WHAT IS OPEN?
Open practice is an emerging field. One characteristic
feature of open researchers is that they often integrate
open elements into what they do.
This can include things like:
● Agile project management
● Directly influencing practice
● Radical transparency
● Social media presence, blogging
● Using networks as a research resource
● Sharing research instruments
● Open access publication
It’s for individual researchers to decide the extent to
which they make their practice open, but many find
that open practices improve the efficiency, reach and
impact of their work.
VITAE
Framework
Open Access
Making scholarly publications available (online) to anyone regardless of their ability to pay
Non-traditional dissemination strategies
Open Data
Making (raw) research data available to anyone for interrogation and reuse
Open Platforms, Tools, Services
Opening access to code and software
Tools to promote efficiency in research and scholarly communication
Open Research
Open and collaborative approaches
Stakeholder involvement
Transparency and Public Enagagement
Raising awareness of research outputs more widely
Values
Duty (Caswell, Henson, Jensen & Wiley, 2008)
Human rights (Blessinger & Bliss, 2016)
Widening participation (Gourley & Lane, 2009)
Commitment to social justice, parity of participation (Hodgkinson-Williams & Trotter, 2018; Lambert, 2018)
Plasticity/Agility: An excellent candidate for sloganizing is the word ‘open.’
Immediately one uses it, the options polarize. To be open (depending on context) is
to be not closed, restricted, prejudiced or clogged; but free, candid, generous, above
board, mentally flexible, future oriented. (Hill, 2010[1975]:2)
Authenticity: "I can list for you any number of examples of companies and
organizations that have attached that word “open” to their products and
services […] All these append “open” to a name without really even trying
to append “openness,” let alone embrace “openness," to their practices or
mission. Whatever “openness” means." (Watters, 2014)
Ethos: Atenas and Havemann (2014) commented: “Notwithstanding the problematic
nature of the term ‘open’ when used in wider contexts, there does seem to be a shared
understanding of an underlying ethos of openness…”
Pragmatism: Not only are there different aspects of openness, but it
may be that some are mutually exclusive with others, or at least that
prioritising some means less emphasis on others. One way to consider
openness is to consider the motivations people have for adopting an open approach. (Weller, 2015:32)
What is
Open
Research?
“Open research is the process of conducting and sharing
research in which a selection of research proposals,
work-process documents, literature reviews,
methodologies, research instruments, analytical frameworks, findings
and/or data are intentionally shared on publically-accessible
platforms in order for others to freely access, use, modify, and share them
subject to measures that preserve ethical practice and legal provenance.”
Hodgkinson-Williams & King (2015:5)
Open
Research
Cycle
Generate
Ideas
Development
Find Funding
Planning
Research
Recording
Data
Analysis
Dissemination
Reading
This is the fuzziest stage where nothing is concrete yet. There is a
balance to be struck at this stage in getting feedback from people
and ‘giving away’ an idea when it becomes specific enough
The main consideration
could be ownership of
ideas that have been
generated openly
It’s important to share
with the right people
and be as open as
appropriate with them
Blogging could be a way
of doing this; social
media provides another
opportunity
Sharing at the ‘ideas’
stage can be a way to
assess the viability of a
research project
Talk to (trusted) people!
You might want to share online, but not have a network to get feedback from – how do you build a network?
Build networks
Context and trust are important
Meet with people who have similar interests
Attend workshops/events and get feedback
Use feedback to develop the idea
into a more feasible project
Networks can be leveraged to identify funding opportunities
Completed grant proposals (successful or unsuccessful) can be shared
for others to learn from. Some research councils make successful proposals
available as a matter of course
Collaborators can be attracted by organisations with transparent working practices
Some funding opportunities mandate open practices, such
as open access dissemination
Involving stakeholders in the process of planning can improve buy-in and encourage a spirit of collaboration
Collaborating in this way could potentially affect the
perception of the objectivity of the researcher
AI
Enhanced
Open
Research
Cycle
Generate
Ideas
Development
Find Funding
Planning
Research
Recording
Data
Analysis
Dissemination
Reading
A balance needs to be
struck here between
expert authority and
stakeholder input
Think about how
openness might affect
the rest of the project
(e.g. ethics)
Sharing initial findings
can improve the
visibility of work
Progress can be shared
through blogs, social
media, newsletters, etc.
If you keep a research
journal, this could be
shared where
appropriate
Inviting critical friends to verify results
Sharing code on GitHub
Sharing analytic frameworks and research instruments
Open access publication of journal papers
Understand licensing options
Sharing (redacted) research data on repositories
Using appropriate metadata to maximise reusability
Need to be especially careful with personal or sensitive information – anticipate how
much needs to be shared to make it useful while protecting participants
Institutional archiving (e.g. ORO)
Compliance with funder requirements
Maximising impact through publishing in alternative channels (e.g. mass media,
short video, alternative media)
Find out what relevant data is already out there ‘in the open’
Tension between peer-reviewed literature and grey literature
Assessment
Use GPT for brainstorming, quick combinations of ideas
Create digital artifacts (infographics, videos, etc.) to support research dissemination
RISKS
AND
ISSUES
Can GenAI meet
expected standards of
attribution for
copyrighted and openly
licensed materials?
Brittain, B. (2023). AI-
generated art cannot
receive copyrights, US
court says. Reuters
(August 21st).
What kind of ethical
guidance is needed?
At what point will AI be
“intelligent” enough to
be useful / cause major
change in education?
Changing paradigms for
creation and derivation
with GenAI
Opportunities
for
researchers
Certification of fair use
of training data
https://
www.fairlytrained.org/
Concepts
Solutionism
Explicable
Artificial
Intelligence
Sociotechnical
(Crawford,
2021)
'Invisible'
Labour
Environmental
Impact
AI and assessments (University of Leeds)
'EXPLOSIVE'
PROPOSITIONS
What we call ‘AI’ is
neither artificial nor
intelligent.
What is intelligence?
What is ‘artificial
intelligence’ - should we
prefer some different
terminology (e.g.
augmented
intelligence)
‘Artificial’ vs traditional
intelligence (Goldsmith)
Does ‘intelligence’ imply
‘goodness’? Nb.
‘normative naturalism’
AI is moving too quickly
for educators and
discipline specialists to
keep up.
Can we equate visibility
of the system(s) as a
whole with ‘openness’?
Many traditional
pedagogies and
assessment techniques
which rely on forms of
generation are no
longer viable.
What is the ‘open’ angle
here?
Open pedagogy & co-
creation of assessment
process
Opportunities for
creative assessment
We (users) will only see
the tip of the AI in
education (AIED)
iceberg
Improved understanding and guidance for the use of AIED is essential
to all discipline areas going forward - we need to update assessment
to work with the existence of machine learning technologies, not
fight against them (Prillaman, 2023).
Opportunities for
creative assessment
Machine learning
algorithms are ‘closed’
and AIED is a place of
openwashing.
Areas where AIED and open educational practices
overlap include: using open algorithms and open data
to support smarter repositories and learning
platforms; developing AI tools for search, discovery,
reuse and sharing of OER; use of algorithms in open
learning environments; new forms of pedagogy;
ameliorating injustice in education; and regulatory/
policy support for “open” AIED (Farrow, 2023; Stacey,
2023).
The need for an
authentically open
version of AIED - how
can we make the
comparison?
“Open the black box!”
The closest thing to
openness in AIED is
‘explicability’ - but this
concept is typically
overstretched.
We need language that
is not just ‘explicable’
from an expert or
technical standpoint,
but explainable and
interpretable to a range
of stakeholders
including learners.
Currently, GPTs act to
transform copyrighted
content, ultimately
making it available to
the public domain -
there is no legal basis
for copywriting the
content that is
produced by a GPT
(Brittain, 2023).
Compiling relevant
court cases could make
an interesting blog post
Do we need a new
licence type? (nb.
Vienna Convention)
Does training an AI
count as ‘distribution’ of
content ?
Despite its rhetoric,
AIED heralds a new
digital divide in
education.
The new digital divide will be between those who have access to AI services and those that do not
(or access inferior services). We’re only at the start of the cycle, but AI services will ultimately be
‘enshittified’ (Doctorow, 2023).
Literature Review
The AIED revolutions have been driven by technology companies. This was
seen most clearly during the Covid19 pandemic where commercial
organisations moved quickly to become providers of learning online. The
pandemic acted as a catalyst for online working, training and education but
also established profit motive and market capture as priorities.
AI broke the internet
and the age of post-
truth is just beginning.
The increasing abilities of generative AI to produce convincing text, audio and
video are going to make the Internet an increasingly problematic source of truth.
Children will grow up using AI tools to understand the world (Aitken, 2023). As in
the case of pedagogical assessment, we need new systems of trust.
The need for new (AI?)
literacies
Building and
maintaining trust
Watermark?
AI agents (Gates, 2023)
offer the prospect of
proactive management
of many tasks and can
be trained on one’s
personal activity - are
you ready to start
training your avatar to
continue your work
posthumously?
Thinking about agency: who is
the agent? How does AI
threaten to mediate or change
our concept of agency?
Can we think of AI as having
any sort of agency? Is it just an
extension of the programmer’s
agency? Something else? This
seems to relate to the issue of
whether AI can be an author of
copyrightable works
AI “end user efficiency” has a significant cost in terms of hidden human labour and environmental
impact. On the surface, AI is touted as a system that saves end users time but behind the scenes of AI
systems are people, often poorly paid, who are labeling data to train it and clarifying data when it
gets confused and an energy-intensive process with a staggering carbon footprint.
https://
www.theverge.com/
features/23764584/ai-
artificial-intelligence-
data-notation-labor-
scale-surge-remotasks-
openai-chatbots
https://earth.org/the-
green-dilemma-can-ai-
fulfil-its-potential-
without-harming-the-
environment/
Prescriptive technologies break whole tasks that disconnect production
and consumption - this has an effect as discussed above in terms of
copyright but it also has the effect of hiding the true costs in terms of
both human and environmental resources associated with their usage
Historical patterns of technological introduction that begin with early adopters engaging in
“liberating” activities that become increasingly prescriptive over time, cars as “mechanical
brides” and the role of telephone operators come to mind
How might AI be used to turn holistic Open Educational Practices into prescriptive, machine-run
activities? What role(s) will/ should open educators play in terms of shaping that future?
Data literacy, data
justice & ethics
https://www.researchgate.net/
publication/377625931_Ethical_Development_of_AI-
Enabled_Open_Educational_Resources_OER#fullTextFileContent
Renewable assessments - should GO-GN be looking at PhDs and thinking about what it means to write a
PhD? How could AI be used? Does using GPTs for writing undermine originality?
Creating an indicator or evaluation framework for assessing the openness/ethical
aspects of a particular instance of GPT used in teaching or research.
Maybe you could interview a few PhD candidates- asking questions around tools,
ethics, copyright infringement, etc and then also use the same or different
students as co-authors? Kind of a students as partners concept?
Courses
MIT
AI
https://ocw.mit.edu/
courses/6-034-artificial-
intelligence-fall-2010/
Uncertainty In
Engineering
https://ocw.mit.edu/
courses/1-010-
uncertainty-in-
engineering-fall-2008/
Introduction To
Network Models
https://ocw.mit.edu/
courses/1-022-
introduction-to-
network-models-
fall-2018/
Techniques In Artificial
Intelligence
https://ocw.mit.edu/
courses/6-825-
techniques-in-artificial-
intelligence-sma-5504-
fall-2002/
Introduction to
Machine Learning
https://
openlearninglibrary.mit.edu/
courses/course-v1:MITx
+6.036+1T2019/about
Alternative
dissemination models
AI Literature Search/
Review
https://elicit.com/
https://
www.semanticscholar.org
https://scite.ai/
assistant?
https://consensus.app/
search/
AI Writing Tools
GPT as editor
How to cite the use of
AI writing tools?
Bozkurt, A. (2024). GenAI et al.: Cocreation, Authorship, Ownership, Academic Ethics
and Integrity in a Time of Generative AI. Open Praxis, 16(1), 1–10.
GPT to improve
grammar and
readability
https://tamu.libguides.com/c.php?g=1289555&p=9679433
AI Research Funding
Tools
https://spinbase.eu/
Acknowledging use of
GenAI in writing
proposals
https://wellcome.org/
what-we-do/our-work/
joint-statement-
generative-ai
ChatGPT
Be aware some versions
are not connected to
online or training data
stops at a certain date
Using GPT to elaborate/
describe a proposal
https://typeset.io/
AI Pattern Recognition
AI Data Visuallisation
Research management
and administration
https://
www.nature.com/
articles/
d41586-023-03277-y
Responsible
AI
Resources
https://pressbooks.pub/
aiforteachers/
What might a publisher
perspective on the the
opportunities/threats
of AI?
The additional workload
caused by GPT authors -
slushpile
Regulation &
Infrastructure
House of Lords Large
language models and
generative AI (Ch.3)
Groupings of the
propositions between
AI / Open / Education:
Tools for
Overcoming
AI Detection
https://humanise.ai/
https://writeme.ai/
https://
www.hyperwriteai.com/
SEO Optimisation
https://rankmath.com/
content-ai/
MIT Technology Review:
What’s next for AI
regulation in 2024?
AI owned by big tech
https://
www.technologyreview.com/2023/12/05/1084393/
make-no-mistake-ai-is-owned-by-big-tech/
https://
exploringaipedagogy.hcommons.org/
New York Times vs
Microsoft
Dropbox has changed
terms and condition to
use stored content to
train LLMs
https://openai.com/
policies/privacy-policy
Lack of agreement over
whether open source
LLMs are a good thing
Openness as route to
safety, auditing, XAI
Open models lacking
safety protocols that
are coded into
commercial LLMs
EU AI Act
https://twitter.com/
pierrepinna/
status/1734006948515733995
https://twitter.com/
percyliang/
status/1720516088864370868
Instructional
Design
Ethical Instructional
Design Helper
By Autummn L Caines
AI
Assurance
https://
papers.ssrn.com/sol3/
papers.cfm?
abstract_id=4660737
On the Dangers of
Stochastic Parrots: Can
Language Models Be
Too Big?
Impact on
labour
Positive
Negative
ChatGPT Is About to
Dump More Work on
Everyone
Critique
ChatGPT Is Dumber
Than You Think
The Stupdiity of AI
Pedagogy
How to Learn and Teach
Economics with Large
Language Models,
Including GPT
Decolonisation
of AI
Artificial Intelligence
and the Feminist
Decolonial Imagination
Artificial intelligence
and consent: a feminist
anti-colonial critique
Venture capital hubris
AI feedback loops risk
'model collapse'
https://
venturebeat.com/ai/
the-ai-feedback-loop-
researchers-warn-of-
model-collapse-as-ai-
trains-on-ai-generated-
content/
Surveillance
Capitalism
https://
reallifemag.com/
friction-free-racism/
http://
www.incidentdatabase.ai/
Reconsidering the
regulation of facial
recognition in public
spaces
When to use human vs
artificial cognition?
Reconsidering the
regulation of facial
recognition in public
spaces
Ethically contentious
aspects of artificial
intelligence
surveillance: a social
science perspective
AI & HE Resources (35 pp.)
Algorithmic
Bias
The Ugly Truth About
Ourselves and Our
Robot Creations: The
Problem of Bias and
Social Inequity
Big Data, Artificial
Intelligence, and
Machine Learning
Algorithms: A
Descriptive Analysis of
the Digital Threats in
the Post-truth Era
State of the art and
practice in AI in
education
https://
clivethompson.medium.com/
warning-labels-for-ai-
generated-text-fa6235481631
New AI tools that can write student essays require educators to rethink teaching and assessment
Blueprint for an AI Bill
of Rights for Education
How AI reduces the
world to stereotypes
Teaching with Text
Generation
Technologies
The Green Dliemma:
Can AI Fulfil Its
Potential Within
Harming the
Environment?
AI Is a Lot of Work
A Generative AI Primer
Artificial Intelligence
and the Future of
Teaching and Learning
Generative Artificial
Intelligence (BC
Campus)
AI for Education
Stracke, C. M., Chounta,
I.- A., Holmes, W., Tlili,
A., & Bozkurt, A. (2023).
A standardised PRISMA-
based protocol for
systematic reviews of
the scientific literature
on Artificial Intelligence
and education (AI&ED).
Using AI to summarise,
parse and analyse
scientific literature
Licensing of training set
data
https://
www.fairlytrained.org/
AI Bubble
https://
pluralistic.net/2023/12/19/
bubblenomics/#pop
Changing
role(s) of
educators
Lack of sustainable
business models
Stakeholder Tensions
Costello, E. (2023).
ChatGPT and the
Educational AI Chatter:
Full of Bullshit or Trying
to Tell Us Something?.
Postdigital Science and
Education, 1-6.
Transformative
potential of AI
Pedró (2019) highlights the potential of AI to transform education,
particularly in developing countries
Intelligent
Tutor
Systems
AI in Education needs
interpretable machine
learning: Lessons from Open
Learner Modelling
Protecting
Learners
AI in education: learner
choice and fundamental
rights
The use of AI in education:
Practicalities and
ethical considerations
The tension between
openness and prudence
in AI research
"Prudence in AI research is not simply a matter of restricting what gets published. Rather, we suggest
that being prudent requires establishing norms and processes for assessing the potential risks of
research, weighing those risks with potential benefits, and managing any resulting actions chosen to
address risks. Though this notion of prudence conflicts with openness in the most absolute sense,
it may be possible to establish processes and new supportive institutions which can mitigate the
greatest risks of malicious use while retaining many of the most important benefits of openness." (p.4)
Responsible AI Systems:
Who are the
Stakeholders?
Bolstered by ever affordable computational power and open big datasets, artificial intelligence (AI) technologies are bringing revolutionary changes to our lives. This article
examines the current trends and elaborates the future potentials of AI in its role for making science more open and accessible. Based on the experience derived from a research
project called Microsoft Academic, the advocates have reasons to be optimistic about the future of open science as the advanced discovery, ranking, and distribution technologies
enabled by AI are offering strong incentives for scientists, funders and research managers to make research articles, data and software freely available and accessible.
Opportunities in Open
Science With AI
Artificial intelligence: Implications for the future of work
Interfacing
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Unleashing the
potential of generative
AI, conversational
agents and chatbots in
educational praxis: A
systematic review and
bibliometric analysis of
GenAI in education.
Open Praxis, 15(4), 261–
270.
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Prompt Engineering
Barrett, Tom. 2023.
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the CREATE
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Use of AI
by
students
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Imaginary
Futures
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Center for Artistic
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Innovation
Cox, Glenda, Michelle Willmers, Robyn Brown, and Michael Held. 2024. “Learning Along the Way: A Case Study on a Pedagogically Innovative Approach to
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Kim, Nam Ju, and Min Kyu Kim. 2022. “Teacher’s Perceptions of Using an Artificial Intelligence-Based Educational Tool for Scientific Writing.”
Frontiers in Education 7: 755914.
Jiahui Luo (Jess) (2024) A critical review of GenAI policies in higher education assessment: a call to reconsider the “originality” of students’
work , Assessment & Evaluation in Higher Education, DOI: 10.1080/02602938.2024.2309963
The Intercept, Raw
Story and AlterNet sue
OpenAI for copyright
infringement
AI Pedagogy Project
How to keep your art
out of AI generators
A Comprehensive
Survey of Datasets for
Large Language
Models:
NIST Researchers
Suggest Historical
Precedent for Ethical AI
Research
OpenAI says New York
Times 'hacked' ChatGPT
to build copyright
lawsuit
AI in education is a
public problem
Generative AI’s
environmental costs are
soaring — and mostly
secret
One Year into ChatGPT:
Resources & Possible
Directions for Educators
in 2024
How to Think About
Remedies in the
Generative AI Copyright
Cases
How journals are
fighting back against a
wave of questionable
images
TextGenEd: Teaching
with Text Generation
Technologies
101 Creative Ideas to
Use AI in Education
Judge crosses out some
claims by writers
against OpenAI, lets
them have another
crack at it
The New York Times’ AI
copyright lawsuit shows
that forgiveness might
not be better than
permission
Reduce time spent on
administration
What kind of trust does
AI deserve, if any?
How empty is
Trustworthy AI? A
discourse analysis of
the Ethics Guidelines of
Trustworthy AI
A new vision of artificial intelligence for the
people https://
www.technologyreview.com/2022/04/22/1050394/
artificial-intelligence-for-the-people/
Why Use
AI in
education?
#1: Academic AI
researchers looking to
optimize educational
outcomes through
engineering solutions -
even field insiders now
worry about their
responsibility
#2: Education policy
officials see AI as a
route to cost saving and
efficiency gains. If the
policy problem is
teacher shortages and
morale, the fix must be
technical - not
structural, political or
economic
#3: AI in education is
serious business.
Edtech, big tech and
their investors are
relying on it after the
post-Covid market
slump. Edtech must
have AI integrations to
get investment - and
therefore to get into
schools/unis
#4: AI in education is an
object of sociological
analysis. It interweaves
with bureaucratic
processes of
institutions and policies
- it's a part of power
networks affecting
education institutions
and individuals
#5: Pragmatists who are
just trying to cope, who
feel like they're "in
some sort of lab
experimenting with our
kids" and are "grappling
with how to stay on top
of constantly evolving
generative AI tools"
How can anyone be
expected to make
reasonable decisions?
Legal Liabilities when
using AI
Air Canada must honor
refund policy invented
by airline’s chatbot
Loweing of academic
standards through GPT
writing
h
Atenas, J., Havemann, L.
& Timmermann, C.
Reframing data ethics
in research methods
education: a pathway to
critical data literacy. Int
J Educ Technol High
Educ 20, 11 (2023).
Algorithmic Justice?
Black, Emily and
Koepke, John Logan and
Kim, Pauline and
Barocas, Solon and Hsu,
Mingwei, Less
Discriminatory
Algorithms (October 2,
2023). Georgetown Law
Journal, Vol. 113, No. 1,
2024, Washington
University in St. Louis
Legal Studies Research
Paper Forthcoming,
Available at SSRN:
https://ssrn.com/
abstract=4590481 or
http://
dx.doi.org/10.2139/
ssrn.4590481
When public policy ‘fails’ and venture capital ‘saves’ education:
Edtech investors as economic and political actors
Make no mistake—AI is
owned by Big Tech
The power of edtech
investors in education
van der Vlist, F., Helmond, A., &
Ferrari, F. (2024). Big AI: Cloud
infrastructure dependence and the
industrialisation of artificial
intelligence. Big Data & Society,
11(1). https://
doi.org/10.1177/20539517241232630
OPENAI quietly deletes
ban on using ChatGPT
for "Miltary and
Warfare"
Lehuedé, Sebastián, An
Elemental Ethics for
Artificial Intelligence:
Water as Resistance
Within AI’s Value Chain
(March 12, 2024). AI &
Society: Journal of
Knowledge, Culture and
Communication,
https://
papers.ssrn.com/sol3/
papers.cfm?
abstract_id=4756794
Dane Leigh Gogoshin (2024) A way
forward for responsibility in the age of
AI, Inquiry. https://
doi.org/10.1080/0020174X.2024.2312455
Workplace AI, robots
and trackers are bad for
quality of life, study
finds
Twitter thread about
exploitation of AI data
workers https://
twitter.com/_KarenHao/
status/1769006784273101074
IMFBlog (2024):
Research shows that AI
can help less
experienced workers
enhance their
productivity more
quickly. Younger
workers may find it
easier to exploit
opportunities, while
older workers could
struggle to adapt. The
effect on labor income
will largely depend on
the extent to which AI
will complement high-
income workers.
"high levels of #academicworkload and
#timepressure predictors of increased #ChatGPT
usage - students under significant academic
#stress are more likely to turn to generative AI
tools for assistance"
Is it harmful or helpful? Examining the causes
and consequences of generative AI usage among
university students Abbas et al. (2024) https://
educationaltechnologyjournal.springeropen.com/
articles/10.1186/s41239-024-00444-7
“use of ChatGPT was
likely to develop
tendencies for
procrastination and
memory loss and
dampen the students’
academic performance”
AI is producing ‘fake’
Indigenous art trained
on real artists’ work
without permission
The Surveillance AI
Pipeline
Pea et al. (2023): Four
key surveillance
technologies within
educational contexts:
Location Tracking;
Facial Identification
Technologies (FITs);
Automated Speech
Recognition; Social
Media Mining. This
paper calls for critical
reflection on how these
surveillance
technologies are
shaping educational
practices, human
development, and
societal norms.
Baker & Hawn (2022):
This research focuses
on the causes,
manifestations, and
implications of
algorithmic bias, as well
as the steps needed to
move from identifying
unknown biases to
achieving fairness and
equity. It is a critical
examination of
algorithmic bias in
educational settings,
highlighting the need
for a concerted effort to
understand, identify,
and mitigate biases to
create more equitable
educational
technologies.
AI
Literacies
AI Literacies are of
crtucial importance if
the route taken is
Explicable AI (XAI) since
transparency isn't
enough for
stakeholders to
understand
"The construction and
operation of AI
algorithms is largely
outside of public view
and without any public
accountability.
Nevertheless, school
people are being
pushed ... to be seen to
be in the forefront of
this alleged digital
revolution" https://
www.colorado.edu/
today/2024/03/21/
researchers-warn-
danger-call-pause-
bringing-ai-schools
Are humans "good"
because they are
intelligent? If so are
humans "better" than
other fellow animals?
Are some humans
"better" than other less
intelligent humans. Are
we having too much
uncritical focus on
"intelligence"?
AI is "full of bullshit" (Costello, 2023;
Frankfurt 1986)
Levels of Openness of AI
models that can have
open source code, open
weights or free access
to hosted versions
Open models can increase
transparency, replicability and
lessen digital divides but imply the
technology is neutral and can lack
guardrails which many commerical
companies will invest in to various
degrees (Bommasani et al 2024
NYT complaint filed this
morning vs OpenAI and
Microsoft
Meta admits using
pirated books to train
AI, but won't pay for it
Should artists be paid
for training data?
OpenAI VP wouldn’t say
OpenAI's CTO was
asked about the
publicly available data
used to train Sora, and
her response
was...unexpectedly
concerning.
‘Impossible’ to create AI
tools like ChatGPT
without copyrighted
material, OpenAI says
Here’s Proof You Can
Train an AI Model
Without Slurping
Copyrighted Content
https://
www.wired.com/story/
proof-you-can-train-ai-
without-slurping-
copyrighted-content/
A need for "more agile, responsive, and
participatory approach to digital literacy framework
development and maintenance" (Tiernan et al 2023)
Counterpoint: AI
literacies if we are not
careful just normalise
AI in education. Ben
Williamson via
Mastadon:
A Machine's ethos? An
inquiry into artificial
ethos and trust
but - are we so confident in these safety protocols given
the history of big tech?
Whittaker, M. (2021). The steep cost of
capture. Interactions, 28(6), 50–55.
Might there also be possibilities for collaborative/community owned-
operated AI that would focus on public good as well as safety?
KEY
READINGS
Mills, A., Bali, M. & Eaton, L. (2023).
How do we respond to generative AI
in education? Open educational
practices give us a framework for an
ongoing process
Holmes, W. (2023). The Unintended Consequences of
Artificial Intelligence and Education
ORCA.nrw - The implications of
artificial intelligence on open
educational resources from
different perspectives
Basgen, B. (August 15, 2023). A Generative AI Primer [web article].
Educause Review
Cardona, M. A., Rodríguez, R. J., & Ishmael, K. (2023). Artificial Intelligence
and the Future of Teaching and Learning [Digital Report]. U.S.
Department of Education, Office of Educational Technology. From
Stracke, C. M., Chounta, I.- A., Holmes, W., Tlili, A., & Bozkurt, A. (2023). A
standardised PRISMA-based protocol for systematic reviews of the
scientific literature on Artificial Intelligence and education (AI&ED).
An Analogy for
Understanding What it
Means for Generative AI
to be “Open”
The GO-GN Mindmap of Open Educational Practices for AI in Education
Suggested Citation: Farrow, R., Atenas, J., Bossu, C., Bozkurt, A., Cáceres-Piñuel,
M., Carson, B., Costello, E., Cox, G., Czerwonogora, A., Dubien, D., Elias, T.,
Essmiller, K., Hackl, C., Havemann, L., Hayman, J., Iniesto, F., Johnson, K.,
Nerantzi, C., Pitt, B., Rabin, E., Roberts, V., Truong, V., Weller, M., Vizgirda, V.,
Yadav, A. (2024). The GO-GN Mindmap of Open Educational Practices for AI in
Education.
https://go-gn.net/
Access the latest version (with hyperlinks) at https://shorturl.at/jAEST
All Images Public Domain except "Open Educational Practices for AI in
Education"; "AI in Teaching and Learning"; "Risks & Issues" & "AI
Dynamite" which are CC BY Bryan Mathers
Makes special
considerations for open
source LLMs
How to Regulate
Unsecured “Open-
Source” AI: No
Exemptions
The EU’s AI Act at a
Crossroads for Rights
EU AI Act: first
regulation on artificial
intelligence | Topics |
European Parliament
(europa.eu)
Artificial Intelligence
Act: MEPs adopt
landmark law | News |
European Parliament
(europa.eu)
The long and winding
road to implement the
AI Act
The Australian
government published
a framework for using
generative AI in
schools: Australian
Framework for
Generative Artificial
Intelligence (AI) in
Schools - Department of
Education, Australian
Government
https://opentextbc.ca/
gettingstarted/chapter/
generative-artificial-
intelligence/
Will Chatbots Teach
Your Children?
How do we respond to
Generative AI in
education: Open
educational practices
give us a framework for
ongoing practices
https://www.ai4t.eu/
textbook
There's no evidence it
works or improves
learning outcomes
The Unintended
Consequences of
Artificial Intelligence
and Education
Education
Technology
Industry's
AI
Principles
Principle 1: AI
technologies in
education should
address the needs of
learners, educators and
families.
Principle 2: AI
technologies used in
education should
account for educational
equity, inclusion and
civil rights as key
elements of successful
learning environments.
Principle 3: AI
technologies used in
education must protect
student privacy and
data.
Principle 4: AI
technologies used in
education should strive
for transparency to
enable the school
community to
effectively understand
and engage with the AI
tools.
Principle 5: Companies
building AI tools for
education should
engage with education
institutions and
stakeholders to explain
and demystify the
opportunities and risks
of new AI technologies.
Principle 6: Education
technology companies
and AI developers
should adopt best
practices for
accountability,
assurance, and ethics,
calibrated to mitigate
risks and achieve the
goals of these
Principles.
Principle 7: The
education technology
industry should work
with the greater
education community
to identify ways to
support AI literacy for
students and educators.
Plagiarism Detection Tools Offer a False Sense of Accuracy
Winner of Japan’s Top Literary Prize Admits She Used
ChatGPT
‘I’m afraid’: critics of anti-cheating technology for students hit by lawsuits
Brew, M., Taylor, S., Lam, R., Havemann, L., & Nerantzi, C. (2023). Towards Developing AI Literacy: Three Student Provocations
on AI in Higher Education. Asian Journal of Distance Education, 18(2), 1-11.
Mollick, Ethan R. and
Mollick, Lilach,
Assigning AI: Seven
Approaches for
Students, with Prompts
(June 12, 2023).
Mollick, Ethan R. and
Mollick, Lilach, Using AI
to Implement Effective
Teaching Strategies in
Classrooms: Five
Strategies, Including
Prompts (March 17,
2023).
Mollick, Ethan R. and
Mollick, Lilach, Practical
AI for Teachers and
Students (Aug 4, 2023)
SAMMELBAND | MÄRZ 2022
Künstliche Intelligenz mit offenen Lernangeboten an Hochschulen
lehren - Erfahrungen und Erkenntnisse aus dem Fellowship-Programm
des KI-Campus (in German)
https://ki-campus.org/
blog/fellowship-
sammelband
AI Tool Collection (in
German Language)
Toolsammlungen für die Erstellung von OER mit generativer KI:
iRights.info: KI und OER:
Wie gut passen sie
zusammen? (in German
Language)
https://irights.info/artikel/kuenstliche-intelligenz-und-open-educational-resources/31872
A Map of Generative AI
for Education | by
Laurence Holt
https://
aieducator.tools/
AI Text Generators:
Sources to Stimulate
Discussion Among
Teachers
KI Campus (The
learning platform for
artificial intelligence)
Courses on AI in English
Language (26)
https://ki-campus.org/overview/course
Courses on AI in
German Language (64)
https://ki-campus.org/overview/course
iMoox (Austrian
Massive Open Course
Platform)
MOOC on Artificial
Intelligence and
Machine Learning
https://imoox.at/course/AI-einformatics?lang=en
MOOC on Societech:
Society in the Context
of Information
Technology (MOOC
generated using AI)
https://imoox.at/mooc/local/landingpage/course.php?shortname=GIKI&lang=en
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