AI Research
RRC getting real with artificial intelligence – Winnipeg Free Press
Red River College Polytechnic is offering crash courses in generative artificial intelligence to help classroom teachers get more comfortable with the technology.
Foundations of Generative AI in Education, a microcredential that takes 15 hours to complete, gives participants guidance to explore AI tools and encourage ethical and effective use of them in schools.
Tyler Steiner was tasked with creating the program in 2023, shortly after the release of ChatGPT — a chatbot that generates human-like replies to prompts within seconds — and numerous copycat programs that have come online since.
MIKE DEAL / FREE PRESS
Lauren Phillips, a RRC Polytech associate dean, said it’s important students know when they can use AI.
“There’s no putting that genie back in the bottle,” said Steiner, a curriculum developer at the post-secondary institute in Winnipeg.
While noting teachers can “lock and block” via pen-and-paper tests and essays, the reality is students are using GenAI outside school and authentic experiential learning should reflect the real world, he said.
Steiner’s advice?
Introduce it with the caveat students should withhold personal information from prompts to protect their privacy, analyze answers for bias and “hallucinations” (false or misleading information) and be wary of over-reliance on technology.
RRC Polytech piloted its first GenAI microcredential little more than a year ago. A total of 109 completion badges have been issued to date.
The majority of early participants in the training program are faculty members at RRC Polytech. The Winnipeg School Division has also covered the tab for about 20 teachers who’ve expressed interest in upskilling.
“There was a lot of fear when GenAI first launched, but we also saw that it had a ton of power and possibility in education,” said Lauren Phillips, associate dean of RRC Polytech’s school of education, arts and sciences.
Phillips called a microcredential “the perfect tool” to familiarize teachers with GenAI in short order, as it is already rapidly changing the kindergarten to Grade 12 and post-secondary education sectors.
Manitoba teachers have told the Free Press they are using chatbots to plan lessons and brainstorm report card comments, among other tasks.
Students are using them to help with everything from breaking down a complex math equation to creating schedules to manage their time. Others have been caught cutting corners.
Submitted assignments should always disclose when an author has used ChatGPT, Copilot or another tool “as a partner,” Phillips said.
She and Steiner said in separate interviews the key to success is providing students with clear instructions about when they can and cannot use this type of technology.
Business administration instructor Nora Sobel plans to spend much of the summer refreshing course content to incorporate their tips; Sobel recently completed all three GenAI microcredentials available on her campus.
Two new ones — Application of Generative AI in Education and Integration of Generative AI in Education — were added to the roster this spring.
Sobel said it is “overwhelming” to navigate this transformative technology, but it’s important to do so because employers will expect graduates to have the know-how to use them properly.
It’s often obvious when a student has used GenAI because their answers are abstract and generic, she said, adding her goal is to release rubrics in 2025-26 with explicit direction surrounding the active rather than passive use of these tools.
“The main idea is not to use the AI tool alone, standalone. You want to complement it with AI literacy training,” the instructor said.
She noted her favourite programs are conversational AI assistant Microsoft Copilot, Perplexity AI (an AI-powered search engine that generates answers with links to references) and Google NotebookLM.
Whereas Copilot and Perplexity AI primarily draw from external sources, Google NotebookLM can analyze trends in original items uploaded by a user.
Registration for RRC Polytech’s next introductory microcredential, running Oct. 6 through Nov. 2, is open. Tuition is $313 per student.
maggie.macintosh@freepress.mb.ca
Maggie Macintosh
Education reporter
Maggie Macintosh reports on education for the Free Press. Originally from Hamilton, Ont., she first reported for the Free Press in 2017. Read more about Maggie.
Funding for the Free Press education reporter comes from the Government of Canada through the Local Journalism Initiative.
Every piece of reporting Maggie produces is reviewed by an editing team before it is posted online or published in print — part of the Free Press‘s tradition, since 1872, of producing reliable independent journalism. Read more about Free Press’s history and mandate, and learn how our newsroom operates.
Our newsroom depends on a growing audience of readers to power our journalism. If you are not a paid reader, please consider becoming a subscriber.
Our newsroom depends on its audience of readers to power our journalism. Thank you for your support.
AI Research
Effects of generative artificial intelligence on cognitive effort and task performance: study protocol for a randomized controlled experiment among college students | Trials
Intervention description {11a}
In the intervention group, the computer screen will be set up in a split-screen format. On the left side of the screen, the participant will receive instructions on the writing prompt, writing requirements, time requirements, grading feedback, and the grading rubric. The instructions will also highlight to the participant that they can use ChatGPT in any way they like to assist their writing, and there is no penalty in their writing score for how ChatGPT is used. The right side of the screen will display a blank ChatGPT interface where the participant can prompt questions and receive answers.
Explanation for the choice of comparators {6b}
In the control group, as in the intervention group, the computer screen will be set up in a split-screen format. On the left side of the screen, the participant will receive the same instructions on the writing prompt, writing requirements, time requirements, grading feedback, and the grading rubric. Additionally, the instructions will highlight to the participant that they can use a text editor in any way they like to assist their writing. On the right side, instead of ChatGPT, a basic text editor interface will be displayed. In summary, this comparator will keep the split-screen format consistent between the two groups and ensure that participants in the control group will complete the writing task with minimal support.
Criteria for discontinuing or modifying allocated interventions {11b}
This study is of minimal risk, and we do not anticipate needing to discontinue or modify the allocated interventions during the experiment. Participants can withdraw from the study at any time.
Strategies to improve adherence to interventions {11c}
Adherence to the interventions will be high because the procedures are straightforward and will be clearly explained in the step-by-step instructions on the computer screen. The participant will be alone in a noise-canceling room during the entire experiment. The participant can reach out to the experimenter through an intercom if they need any clarification.
Relevant concomitant care permitted or prohibited during the trial {11d}
Not applicable. This is not a clinical study.
Provisions for post-trial care {30}
Not applicable. This is a minimal-risk study.
Outcomes {12}
The study has two primary outcomes. First, we will measure participants’ writing performance scores on the analytical writing task. The task is adapted from the Analytical Writing section in the GRE, a worldwide standardized computer-based exam developed by the Educational Testing Service (ETS) [27]. Participants’ writing performance will be scored using the GRE 0–6 rubric and by an automatic essay-scoring platform called ScoreItNow!, which is powered by ETS’s e-rater engine [32, 33]. We chose to adapt from the GRE writing materials for two reasons. First, their writing task and grading rubrics were established writing materials designed to measure critical thinking and analytical writing skills and have been used in research as practice materials for writing (e.g. [34]). Second, OpenAI’s technical report shows that ChatGPT (GPT-4) can score 4 out of 6 (~ 54th percentile) on the GRE analytical writing task [31]. This gives us a benchmark for assessing the potential increase in writing performance when individuals collaborate with generative AI.
Second, we will measure participants’ cognitive effort during the writing process. Participants’ cognitive effort will be measured using a psychophysiological proxy—i.e., changes in pupil size [35, 36]. Pupil diameter and gaze data will be collected using the Tobii Pro Fusion eye tracker at a sampling rate of 120 Hz. During the preparation stage of the study, the room light will be adjusted so that the illuminance at the participants’ eyes is at a constant value of 320 LUX. Baseline pupil diameters will be recorded during a resting task in the experiment preparation stage that asks the participant to stare at a cross that will appear for 10 s each on the left, center, and right sections of the computer screen. Pupil diameters and gaze data will be recorded throughout the writing process.
The study has several secondary outcomes. First, to identify the neural substrates of cognitive effort during the writing process, we developed an additional psychophysiological proxy, changes in the cortical hemodynamic activity in the frontal lobe of the brain. Specifically, we will examine hemodynamic changes in oxyhemoglobin (HbO). Brain activity will be recorded throughout the writing process using the NIRSport 2 fNIRS device and the Aurora software with a predefined montage (Fig. 2). The montage consists of eight sources, eight detectors, and eight short-distance detectors. The 18 long-distance channels (source-detector distance of 30 mm) and eight short-distance channels (source-detector distance of 8 mm) are located over the prefrontal cortex (PFC) and supplementary motor area (SMA) (Fig. 2). The PFC is often involved in executive function (e.g., cognitive control, cognitive efforts, inhibition) [37, 38]. The SMA is associated with cognitive effort [39, 40]. The sampling rate of the fNIRS is 10.2 Hz. Available fNIRS cap sizes are 54 cm, 56 cm, and 58 cm. The cap size selected will always be rounded down to the nearest available size based on the participant’s head measurement. The cap is placed on the center of the participant’s head based on the Cz point from the 10–20 system.
Third, we will measure participants’ subjective perceptions of the writing task by self-reported survey measures in the post-survey (Table 1). We will measure participants’ subjective perceptions of the two primary outcomes—that is, their self-perceived writing performance and self-perceived cognitive effort. Self-perceived writing performance will be measured with a one-item scale using the same grading rubric described in the instructions for their writing task and used in the scoring tool. Self-perceived cognitive effort will be measured using a one-item scale adapted from the National Aeronautics and Space Administration-task load index (NASA-TLX) [41, 42]. We will also measure participants’ subjective perceptions of several mental health and learning-related outcomes, including stress, challenge, and self-efficacy in writing. Self-perceived stress will be measured using a one-item scale adapted from the Primary Appraisal Secondary Appraisal scale (PASA) [43, 44]. Self-perceived challenge will be measured using a one-item sub-scale adapted from the Primary Appraisal Secondary Appraisal scale (PASA) [43, 44]. Self-efficacy in writing will be measured using a 16-item scale that measures three dimensions of writing self-efficacy: ideation, convention, and self-regulation [45]. Furthermore, we will measure participants’ situational interest in analytical writing using a four-item Likert scale adapted from the situational interest scale [46]. Additionally, we will measure participants’ behavioral intention to use ChatGPT in the future for essay writing tasks [47].
Participant timeline {13}
The time schedule is provided via the schematic diagram below (Fig. 3). The entire experiment will last for approximately 1–1.5 h for each participant.
Sample size {14}
To estimate the required sample size, we conducted a simulation analysis on the intervention effect on writing performance using ordinary least squares (OLS) regression. Recent empirical evidence suggests that the effect size of generative AI on writing tasks ranges around Cohen’s d = 0.4–0.5, such as [1, 48]. In our simulation analysis, the simulated data assumes normally distributed data, equal and standardized standard deviations between the two conditions, and an anticipated effect size of Cohen’s d = 0.45. In the end, our analysis indicated that recruiting a minimum of 160 participants would be necessary to achieve a statistical power greater than 0.8 under an alpha level of 0.05. The simulation was implemented in R, and the corresponding code is available at the Open Science Framework (OSF) via https://osf.io/9jgme/.
We opt to base our sample size estimation on writing performance, but not on the other primary outcome, cognitive effort, for two reasons. First, the effect of generative AI on performance outcomes has been studied [1, 48], but we did not find prior evidence on the effect size of generative AI on cognitive effort using physiological measures. Second, our physiological measure of cognitive effort may likely be powered once the sample size satisfies our behavioral measure of writing performance. Pupillometry studies on cognitive efforts, such as the N-back test, typically recruit 20–50 participants in short, repeated, within-subject trials (e.g., [49]). These studies provide a general estimation of participants needed. Although our study design (i.e., a between-subject RCT) differs from common pupillometry studies, cognitive effort is still a repeated outcome measure using time series pupil data throughout the entire writing process. Repeated outcome measures generally can enhance statistical power by taking into account within-subject variability [50].
Recruitment {15}
The recruitment will follow a convenience sampling strategy. To aim for a student population with diverse academic backgrounds, participants will be recruited broadly through social media platforms, email lists, and flyers at the research university where the experiment will be conducted. Given that the experiment will start during the summer, the research team can recruit summer school students as participants. Thus, the study sample will not be limited to the students presently at the university. The recruitment materials include a brief description of the study, the eligibility criteria for participation, and the compensation for participation. Individuals who are interested in participation can sign up on a calendar by selecting available time slots provided by the experimenters. Participants will receive 30 euros in compensation upon completion of the experiment. Participants who withdraw in the middle of the experiment will receive partial compensation, prorated based on the amount of time they spend in the experiment.
AI Research
Canadian Scientists Pioneer Made-in-Canada Quantum-powered AI Solution
Insider Brief
- A Canadian-led research team from TRIUMF and the Perimeter Institute has developed a quantum-assisted AI model to simulate particle collisions more efficiently, addressing global computational challenges.
- The study demonstrates that combining deep learning with quantum computing—using technology from D-Wave—can significantly reduce the time and cost of high-energy physics simulations.
- Published in npj Quantum Information, the work supports future upgrades to CERN’s Large Hadron Collider and underscores Canada’s growing leadership in quantum and AI-driven scientific research.
PRESS RELEASE — In a landmark achievement for Canadian science, a team of scientists led by TRIUMF and the Perimeter Institute for Theoretical Physics have unveiled transformative research that – for the first time – merges quantum computing techniques with advanced AI to model complex simulations in a fast, accurate and energy-efficient way.
“This is a uniquely Canadian success story,” said Wojciech Fedorko, Deputy Department Head, Scientific Computing at TRIUMF. “Uniting the expertise from our country’s research institutions and industry leaders has not only advanced our ability to carry out fundamental research, but also demonstrated Canada’s ability to lead the world in quantum and AI innovation.”
Traditional simulations of particle collisions are already both time-consuming and costly, often running on massive supercomputers for weeks or months. By leveraging quantum processes and technology made possible by California-based D-Wave Quantum Inc., the researchers were able to create a new “quantum-assisted” generative model capable of running simulations and open new opportunities to cost-effectively analyze rapidly growing data sets.
The research, published today in npj Quantum Information, is part of a worldwide effort to create the tools needed to accommodate upgrades to CERN’s particle accelerator, the Large Hadron Collider (LHC), and alleviate a computational bottleneck that would impact researchers all over the world.
“Our method shows that quantum and AI technologies developed here in Canada can solve real-world scientific bottlenecks,” said Javier Toledo-Marín, joint appointee at TRIUMF and Perimeter Institute. “By combining deep learning with quantum technology, we are forging a new path for both theoretical experimentation and technological application.”
In addition to TRIUMF and Perimeter, contributions to the published research came from the National Research Council of Canada (NRC), the University of British Columbia and the University of Virginia, showcasing not only the wealth of research talent and scientific ingenuity across the country, but also the international collaboration that places Canada at the forefront of worldwide scientific innovation.
AI Research
Apple Researchers Create an AI Model That Uses Behavioural Data from Wearables to Predict Health Signals
Apple researchers, in collaboration with the University of Southern California, have developed a new artificial intelligence (AI) model that tracks behavioural data over sensor signals. The new research builds on prior work by the Apple Heart and Movement Study (AHMS) and was aimed at understanding if behavioural data, such as sleep pattern and step count, can be a better determinant of a person’s health compared to traditional indices such as heart rate and blood oxygen level. As per the paper, the AI model performed surprisingly well, even if with some caveats.
New Apple Study Shows Benefits of Moving Beyond Traditional Health Data
The study, titled “Beyond Sensor Data: Foundation Models of Behavioral Data from Wearables Improve Health Predictions” was published in the pre-print journal arXiv and is yet to be peer reviewed. The researchers set out to develop an AI model, dubbed Wearable Behaviour Model (WBM), that relies on processed behavioural data from wearables such as how long a person sleeps and their REM cycles, daily steps taken and gait, and how their activity pattern changes over the week.
Traditionally, to predict or assess someone’s health, wearable health research has typically focused on raw sensor readings such as continuous heart rate monitoring, blood oxygen levels, and body temperature. The study believes that while this data can be useful at times, it also lacks the full context about the individual and can have inconsistencies.
Regardless, so far, behavioural data, which is also something most wearables process, has not been used in systems as a reliable indicator of a person’s health. There are two main reasons for it, according to the study. First, this data is much more voluminous compared to sensor data, and as a result, it can also be very noisy. Second, creating algorithms and systems that can collect and analyse this data and reliably make health predictions is very challenging.
This is where a large language model (LLM) comes in and solves the analysis problem. To solve the noise in data, researchers fed the model with structured and processed data. The data itself comes from more than 1,62,000 Apple Watch users who participated in the AHMS research, totalling more than 2.5 billion hours of wearable data.
Once trained, the AI model used 27 different behavioural metrics, which were grouped into categories such as activity, cardiovascular health, sleep, and mobility. It was then tested across 57 different health-related tasks, such as finding out if someone had a particular medical condition (diabetes or heart disease) and tracking temporary health changes (recovery from injury or infection). Compared to the baseline accuracy, researchers claimed that WMB outperformed in 39 out of 47 outcomes.
Comparison between performance of the WBM model the test model and the combination of both
Photo Credit: Apple
The findings from the model were then compared with another test model that was only fed raw heart data, also known as photoplethysmogram (PPG) data. Interestingly, when individually compared, there was no clear winner. However, when researchers combined the two models, the accuracy of prediction and health analysis was measured to be higher.
Researchers believe combining traditional sensor data with behavioural data could improve the accuracy in the prediction of health conditions. The study stated that behavioural data metrics are easier of interpret, align better with real-life health outcomes, and are less affected by technical errors.
Notably, the study also highlighted several key limitations. The data was taken from Apple Watch users in the US, and the broader global population was not represented in this. Additionally, due to the high price of wearable devices that accurately collect and store behavioural data, accessibility of preventive healthcare also becomes a challenge.
-
Funding & Business2 weeks ago
Kayak and Expedia race to build AI travel agents that turn social posts into itineraries
-
Jobs & Careers1 week ago
Mumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Mergers & Acquisitions1 week ago
Donald Trump suggests US government review subsidies to Elon Musk’s companies
-
Funding & Business1 week ago
Rethinking Venture Capital’s Talent Pipeline
-
Jobs & Careers1 week ago
Why Agentic AI Isn’t Pure Hype (And What Skeptics Aren’t Seeing Yet)
-
Education4 days ago
9 AI Ethics Scenarios (and What School Librarians Would Do)
-
Education1 week ago
AERDF highlights the latest PreK-12 discoveries and inventions
-
Education4 days ago
Teachers see online learning as critical for workforce readiness in 2025
-
Education5 days ago
Nursery teachers to get £4,500 to work in disadvantaged areas
-
Education6 days ago
How ChatGPT is breaking higher education, explained