In late 2024, we will wind down the Jamboard whiteboarding app as well as continue with the previously planned end of support for Google Jamboard devices. For those who are impacted by this change, we are committed to help you transition:
We are integrating whiteboard tools such as FigJam, Lucidspark, and Miro across Google Workspace so you can include them when collaborating in Meet, sharing content in Drive, or scheduling in Calendar.
Instructional Design Careers and Freelancing Presentations — from christytuckerlearning.com by Christy Tucker A collection of my presentations and podcasts on instructional design careers and freelancing, including transitioning from teaching to ID.
Scenario 1: Fully Digitalized Law School Scenario 2: Blended Law School Experience Scenario 3: Specialized Legal Education Scenario 4: Decentralized Legal Education
In the decentralized legal education scenario, the traditional model of law schools is disrupted by the emergence of alternative education platforms and micro-credentialing. The concept of a law degree is replaced by a more flexible and personalized approach to legal education. Students can choose from an array of legal courses offered by various providers, including universities, law firms, online platforms, and even government agencies.
Calculus is a critical on-ramp to careers in science, technology, engineering and mathematics (STEM). But getting to those careers means surviving the academic journey.
While there’s been progress of late, it’s been “uneven” and Black, Hispanic and women workers are still underrepresented in some STEM fields. Traditional methods of calculus instruction may be knocking students off the path to these vital occupations, which is why advocates warn that getting diverse students into these careers may require instructional models more responsive to students. Meanwhile, the country is struggling to fill vacancies in related fields like semiconductor manufacturing, despite sizable investments — a feat that may require stabilizing the pipeline.
Good news: There’s mounting evidence that changing calculus instruction works for the groups usually pushed out of STEM. At least, that’s according to a randomized study recently published in the peer-reviewed journal Science.
Student Use Cases for AI: Start by Sharing These Guidelines with Your Class — from hbsp.harvard.edu by Ethan Mollick and Lilach Mollick
To help you explore some of the ways students can use this disruptive new technology to improve their learning—while making your job easier and more effective—we’ve written a series of articles that examine the following student use cases:
Earlier this week, CETL and AIG hosted a discussion among UM faculty and other instructors about teaching and AI this fall semester. We wanted to know what was working when it came to policies and assignments that responded to generative AI technologies like ChatGPT, Google Bard, Midjourney, DALL-E, and more. We were also interested in hearing what wasn’t working, as well as questions and concerns that the university community had about teaching and AI.
Then, in class he put them into groups where they worked together to generate a 500-word essay on “Why I Write” entirely through ChatGPT. Each group had complete freedom in how they chose to use the tool. The key: They were asked to evaluate their essay on how well it offered a personal perspective and demonstrated a critical reading of the piece. Weiss also graded each ChatGPT-written essay and included an explanation of why he came up with that particular grade.
After that, the students were asked to record their observations on the experiment on the discussion board. Then they came together again as a class to discuss the experiment.
Weiss shared some of his students’ comments with me (with their approval). Here are a few:
Asked to describe the state of generative AI that they would like to see in higher education 10 years from now, panelists collaboratively constructed their preferred future. .
Julie York, a computer science and media teacher at South Portland High School in Maine, was scouring the internet for discussion tools for her class when she found TeachFX. An AI tool that takes recorded audio from a classroom and turns it into data about who talked and for how long, it seemed like a cool way for York to discuss issues of data privacy, consent and bias with her students. But York soon realized that TeachFX was meant for much more.
York found that TeachFX listened to her very carefully, and generated a detailed feedback report on her specific teaching style. York was hooked, in part because she says her school administration simply doesn’t have the time to observe teachers while tending to several other pressing concerns.
“I rarely ever get feedback on my teaching style. This was giving me 100 percent quantifiable data on how many questions I asked and how often I asked them in a 90-minute class,” York says. “It’s not a rubric. It’s a reflection.”
TeachFX is easy to use, York says. It’s as simple as switching on a recording device.
…
But TeachFX, she adds, is focused not on her students’ achievements, but instead on her performance as a teacher.
ChatGPT Is Landing Kids in the Principal’s Office, Survey Finds — from the74million.org by Mark Keierleber While educators worry that students are using generative AI to cheat, a new report finds students are turning to the tool more for personal problems.
Indeed, 58% of students, and 72% of those in special education, said they’ve used generative AI during the 2022-23 academic year, just not primarily for the reasons that teachers fear most. Among youth who completed the nationally representative survey, just 23% said they used it for academic purposes and 19% said they’ve used the tools to help them write and submit a paper. Instead, 29% reported having used it to deal with anxiety or mental health issues, 22% for issues with friends and 16% for family conflicts.
Part of the disconnect dividing teachers and students, researchers found, may come down to gray areas. Just 40% of parents said they or their child were given guidance on ways they can use generative AI without running afoul of school rules. Only 24% of teachers say they’ve been trained on how to respond if they suspect a student used generative AI to cheat.
The prospect of AI-powered, tailored, on-demand learning and performance support is exhilarating: It starts with traditional digital learning made into fully adaptive learning experiences, which would adjust to strengths and weaknesses for each individual learner. The possibilities extend all the way through to simulations and augmented reality, an environment to put into practice knowledge and skills, whether as individuals or working in a team simulation. The possibilities are immense.
Thanks to generative AI, such visions are transitioning from fiction to reality.
Video: Unleashing the Power of AI in L&D — from drphilippahardman.substack.com by Dr. Philippa Hardman An exclusive video walkthrough of my keynote at Sweden’s national L&D conference this week
Highlights
The wicked problem of L&D: last year, $371 billion was spent on workplace training globally, but only 12% of employees apply what they learn in the workplace
An innovative approach to L&D: when Mastery Learning is used to design & deliver workplace training, the rate of “transfer” (i.e. behaviour change & application) is 67%
AI 101: quick summary of classification, generative and interactive AI and its uses in L&D
The impact of AI: my initial research shows that AI has the potential to scale Mastery Learning and, in the process:
reduce the “time to training design” by 94% > faster
reduce the cost of training design by 92% > cheaper
increase the quality of learning design & delivery by 96% > better
Research also shows that the vast majority of workplaces are using AI only to “oil the machine” rather than innovate and improve our processes & practices
Practical tips: how to get started on your AI journey in your company, and a glimpse of what L&D roles might look like in a post-AI world
I dwell on this story not merely because the irony of defending the role of research by ignoring the research on the topic is exquisite, but because it is emblematic of a widespread problem within higher education. The resistance to anything like serious change is profound. By “change” I don’t mean the addition of yet another program or the alteration of a graduation requirement, but something that is transformational and affects the way we do our work on a deep level.
If maintenance of the status quo is the goal, higher education has managed to create the ideal system.
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Cut through all the graphs and economic data and the problem is straightforward: When the service you provide costs more than people are willing and able to pay for it, when you are unable to lower the cost of that service, and when the number of your potential customers is shrinking, you have what one might describe as an unsustainable financial model.
…
“College teaching has probably seen less change than almost any other American institutional practice since the days of Henry Adams.”
Preparing Students for the AI-Enhanced Workforce— from insidehighered.com by Ray Schroeder Our graduating and certificate-completing students need documented generative AI skills, and they need them now.
The common adage repeated again and again is that AI will not take your job; a person with AI skills will replace you. The learners we are teaching this fall who will be entering, re-entering or seeking advancement in the workforce at the end of the year or in the spring must become demonstrably skilled in using generative AI. The vast majority of white-collar jobs will demand the efficiencies and flexibilities defined by generative AI now and in the future. As higher education institutions, we will be called upon to document and validate generative AI skills.
Think all incoming teachers have a natural facility with technology just because most are digital natives? Think again.
Teacher preparation programs have a long way to go in preparing prospective educators to teach with technology, according to a report released September 12 by the International Society for Technology in Education, a nonprofit.
In fact, more than half of incoming teachers—56 percent—lack confidence in using learning technology prior to entering the classroom, according to survey data included with the report.
AI-Powered Higher Ed — from drphilippahardman.substack.com by Dr. Philippa Hardman What a House of Commons round table discussion tells us about how AI will impact the purpose of higher education
In this week’s blog post I’ll summarise the discussion and share what we agreed would be the most likely new model of assessment in HE in the post-AI world.
But this in turn raises a bigger question: why do people go to university, and what is the role of higher education in the twenty first century? Is it to create the workforce of the future? Or an institution for developing deep and original domain expertise? Can and should it be both?
In my previous position with Richmond Public Schools, we chose to dive in with computational thinking, programming and coding, in that order. I recommend building computational thinking (CT) competency first by helping students recognize and apply the four elements of CT to familiar problems/situations. Computational thinking should come first because it’s the highest order of problem-solving, is a cross-curricular skill and is understandable to both machines and humans. Here are the four components of CT and how to help students understand them.
So much of the way that we think about education and work is organized into silos. Sure, that’s one way to ensure a depth of knowledge in a field and to encourage learners to develop mastery. But it also leads to domains with strict boundaries. Colleges are typically organized into school sub-domains, managed like fiefdoms, with strict rules for professors who can teach in different schools.
Yet it’s at the intersections of seemingly-disparate domains where breakthrough innovation can occur.
Maybe intersections bring a greater chance of future work opportunity, because that young person can increase their focus in one arena or another as they discover new options for work — and because this is what meaningful work in the future is going to look like.
From DSC: This posting strikes me as an endorsement for interdisciplinary degrees. I agree with much of this. It’s just hard to find the right combination of disciplines. But I supposed that depends upon the individual student and what he/she is passionate or curious about.
A lot of people have been asking if AI is really a big deal for the future of work. We have a new paper that strongly suggests the answer is YES. .
Consultants using AI finished 12.2% more tasks on average, completed tasks 25.1% more quickly, and produced 40% higher quality results than those without. Those are some very big impacts. Now, let’s add in the nuance.
The bigger picture: Culture is exceedingly hard to change, especially after trust evaporates in a crisis. And crises can be particularly wrenching when institutions haven’t established cultures of transparency and accountability during good times. Instead, trust continues to erode in a vicious cycle.
And I thought to myself, the scope of Rick’s conclusion could likely be expanded/applied to institutions of higher education as a whole.
One of the biggest challenges to navigate now is the fact that more digital tools will come with generative AI already embedded in them, says Annette Vee, director of composition and an associate professor at the University of Pittsburgh. “It’s everywhere in professional writing.”
“We need to be fundamentally rethinking ways we teach writing, so we are thinking about integrating tools mindfully,” says Vee, who helped develop a new resource, TextGenEd, that provides guidance in this area. “The real challenge is how do we teach courses that are preparing students and that are smart about generative AI? We have very few teachers currently equipped to do that work.”
“It’s best if there are real stakes attached to the work, for example, an authentic audience the student is writing to,” he writes. “A subject on which students have both sufficient interest and knowledge in order to feel as though they can write convincingly to this audience also matters a lot.”
Also relevant/see — via Robert Gibson on LinkedIn:
Learnt.ai — Built for Learning Specialists — from learnt.ai Harness the power of artificial intelligence to enhance your learning and development efforts with our easy-to-use platform – no technical expertise required!
Introducing Learnt.ai – a revolutionary collection of AI-powered content generation tools and AI chatbots that are specifically designed to support the common writing tasks of educationalists and learning and development professionals. Imagine being able to generate learning objectives on any topic of your choice, create engaging icebreakers and activities, write assessment questions with ease, and so much more.
Students are using artificial intelligence tools to assist them in their academic careers. Three students share their viewpoints on the tools they use and how using these tools helps them in their coursework and prepares them for the professional world.
Also relevant/see:
Why Professors Are Polarized on AI — from insidehighered.com by Susan D’Agostino Academics who perceive threats to education from AI band together as a survival mechanism. The resulting alliances echo divisions formed during online learning’s emergence.
Ask the Chair: Are Great Chairs Born or Made? — from chronicle.com by Kevin Dettmar (behind a paywall) Higher education is finally getting serious about training new department heads.
Great chairs aren’t born, but made; “trial and error” isn’t actually a professional-development strategy. The provost and deans should recognize that a confident and competent chair makes their job easier, creates a well-functioning department, and buoys faculty, student, and staff morale.
As someone vitally engaged with the chair’s role, I do think we are experiencing a sea-change when it comes to how institutions are preparing chairs. For too long, colleges have treated the position as simply a minor cog in the chain of command. But more and more institutions are now investing in their chairs.
Why do so many students have the impression that they should attend office hours only if they’ve got a question? Here’s my hunch: Well-meaning, supportive professors mention their office hours at various points throughout the course: If you have questions about fill-in-the-blank, come to my office hours. The professors mean, “I am here to help! Come talk to me.” Students hear: “If you have a question.”
It’s a frustrating misunderstanding, because it contributes to the big problem my article focuses on: Many students miss out on the support professors stand to provide.
… ‘Adjunct Faculty 101’
“Sessions offered this year included ‘An Orientation to the Canvas LMS,’ ‘The First Day of Class,’ ‘Creating Student-Centered Course Materials,’ ‘Classroom-Management Tips,’ ‘Academic-Integrity Tips and Processes,’ ‘An Introduction to Our Tutoring Services and Writing Center,’ ‘Writing Across the Disciplines With AI,’ and more.”
From DSC: I like the idea of using “office hours” for building relationships, helping students with their future career decisions, building broader understanding, ongoing mentoring, and for developing potential networking opportunities.
6. Create externship programs for faculty. Many college and university faculty have never worked outside of academia. Given a chance to be exposed to modern workplaces and work challenges, faculty will find innovative and creative ways to weave more work-integrated learning into their curriculum.
From DSC: This is a great idea — thanks Brandon!
I might add another couple of thoughts here as well:
And/or treat your Adjunct Faculty Members much better as well!
And/or work with more L&D Departments at local companies (i.e., to develop closer, more beneficial/WIN-WIN collaborations).
What does active learning require from students? There is no secret that PBL and all other active learning approaches are much more demanding from students compared to traditional methods, mainly in terms of skills and attitudes towards learning. Here are some of the aspects where students, especially when first faced to active learning, seem to struggle:
Formulating own learning goals and following through with independent study. While in traditional teaching the learning goals are given to students, in PBL (or at least in some of its purest variants), they need to come up with their own, for each problem they are solving. This requires understanding the problem well but also a certain frame of mind where one can assess what is necessary to solve it and make a plan of how to go about it (independently and as a group). All these seemingly easy steps are often new to students and something they intrinsically expect from us as educators.
From DSC: The above excerpt re: formulating one’s own learning goals reminded me of project management and learning how to be a project manager.
It reminded me of a project that I was assigned back at Kraft (actually Kraft General Foods at the time). It was an online-based directory of everyone in the company at the time. When it was given to me, several questions arose in my mind:
Where do I start?
How do I even organize this project?
What is the list of to-do’s?
Who will I need to work with?
Luckily I had a mentor/guide who helped me get going and an excellent contact with the vendor who educated me and helped me get the ball rolling.
I’ll end with another quote and a brief comment:
Not being afraid of mistakes and learning from them. The education system, at all stages, still penalises mistakes, often with long term consequences. So it’s no wonder students are afraid of making mistakes…
What we teachers desperately need, though, is an ocean of examples and training. We need to see and share examples of generative AI—any type of artificial intelligence that can be used to create new text, images, video, audio, code, or data—being used across the curriculum. We need catalogs of new lesson plans and new curriculum.
And we need training on theoretical and practical levels: training to understand what artificial intelligence actually is and where it stands in the development timeline and training about how to integrate it into our classes.
So, my advice to teachers is to use any and all the generative AI you can get your hands on. Then experience—for yourself—verification of the information. Track it back to the source because in doing so, you’ll land on the adjustments you need to make in your classes next year.
From DSC: Interesting.
Learners can now seamlessly transition between AI-powered assistance (AI Tutor) and Live Expert support to get access to instant support, whether through AI-guided learning or real-time interactions with a human expert.
ASSIGNMENT MAKEOVERS IN THE AI AGE WITH DEREK BRUFF — from teachinginhighered.com by Bonni Stachowiak Derek Bruff shares about assignment makeovers in the AI age on episode 481 of the Teaching in Higher Ed podcast
Comment on this per Derek Bruff:
Why not ask ChatGPT to write what King or X would say about a current debate and then have the students critique the ChatGPT output? That would meet the same learning goals while also teaching AI literacy.
(Be sure to read Asim’s contribution for a useful take.)
Here’s a closer look at the concurrent AI landscape in schools — and a prediction of what the future holds.
So far, high-profile ventures in the instruction realm, such asKyron Learning, have fused teacher-produced, recorded content with LLM-powered conversational UX. The micro-learning tool Nolej references internet material when generating tasks and tests, but always holds the language model closely to the ground truth provided by teachers. Both are intriguing takes on re-imagining how to deliver core instruction and avoid hallucinations (generated content that is nonsensical).
As a result, real-time 3D jobs are among the most in demand within the tech industry. According to Unity’s vice president of Education and Social Impact, Jessica Lindl, demand is 50% higher than traditional IT jobs—adding that salaries for real-time 3D jobs are 60% greater.
“We want to provide really simple on ramps and pathways that will lead you into entry level jobs so that at any point in your career, you can decide to transfer into the industry,” Lindl says.
University World News continues its exploration of generative AI in our new special report on ‘AI and Higher Education’. In commentaries and features, academics and our journalists around the world investigate issues and developments around AI that are impacting on universities. Generative AI tools are challenging and changing higher education systems and institutions — how they are run as well as ways of teaching and learning and conducting research.
My advice for you today is this: fill your LinkedIn-feed and/or inbox with ideas, inspirational writing and commentary on AI.
This will get you up to speed quickly and is a great way to stay informed on the newest movements you need to be aware of.
My personal recommendation for you is to check out these bright people who are all very active on LinkedIn and/or have a newsletter worth paying attention to.
I have kept the list fairly short – only 15 people – in order to make it as easy as possible for you to begin exploring.
It is crucial to recognize that the intrinsic value of higher education isn’t purely in its ability to adapt to market fluctuations or technological innovations. Its core strength lies in promoting critical thinking, nurturing creativity, and instilling a sense of purpose and belonging. As AI progresses, these traits will likely become even more crucial. The question then becomes if higher education institutions as we know them today are the ony ones, or indeed the best ones, equipped to convey those core strengths to students.
Higher education clearly finds itself caught in a whirlwind of transformation, both in its essence and execution. The juxtaposition of legacy structures and the evolving technological landscape paints a complex picture.
For institutional leaders, the dual challenge lies in proactively seeking and initiating change (not merely adapting to it) without losing sight of their foundational principles. Simultaneously, they must equip students with skills and perspectives that AI cannot replicate.
“They begged, bargained with, and berated their instructor in pursuit of better grades — not “because they like points,” but rather, “because the education system has told them that these points are the currency with which they can buy a successful future.””
