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How artificial intelligence will impact K-12 teachers
While most teachers report enjoying their work, they do not report enjoying the late nights marking papers, preparing lesson plans, or filling out endless paperwork.
Further disheartening to teachers is the news that some education professors have even gone so far as to suggest that teachers can be replaced by robots, computers, and artificial intelligence (AI).
John von Radowitz, “Intelligent machines will replace teachers within 10 years, leading public school headteacher predicts,”
The McKinsey Global Institute’s 2018 report on the future of worksuggests that, despite the dire predictions, teachers are not going away any time soon.
Our current research suggests that 20 to 40 percent of current teacher hours are spent on activities that could be automated using existing technology.
That translates into approximately 13 hours per week that teachers could redirect toward activities that lead to higher student outcomes and higher teacher satisfaction.
In short, our research suggests that existing technology can help teachers reallocate 20 to 40 percent of their time to activities that support student learning.
Further advances in technology could push this number higher and result in changes to classroom structure and learning modalities, but are unlikely to displace teachers in the foreseeable future.
Many of the attributes that make good teachers great are the very things that AI or other technology fails to emulate: inspiring students, building positive school and class climates, resolving conflicts, creating connection and belonging, seeing the world from the perspective of individual students, and mentoring and coaching students.
Research suggests that simply having an effective kindergarten teacher can affect the likelihood of a student completing college thus boosting their lifetime earnings by about $320,000.
In the remainder of this article, we will outline how teachers spend their time today, how technology can help to save teacher time, and where that additional time might go.
To understand how teachers are spending their time today and how that might change in a more automated world, we surveyed more than 2,000 teachers in four countries with high adoption rates for education technology: Canada, Singapore, the United Kingdom, and the United States.
We asked teachers how much time they spend on 37 core activities, from lesson planning to teaching to grading to maintaining student records.
We asked what technologies teachers and students were currently using in the classroom to discover new content, practice skills, and provide feedback.
Our findings were unequivocal: teachers, across the board, were spending less time in direct instruction and engagement than in preparation, evaluation, and administrative duties (Exhibit 1).
Once we understood how teachers spend their time, we evaluated automation potential across each activity, based on an evaluation of existing technology and expert interviews.
Even if teachers spend the same amount of time preparing, technology could make that time more effective, helping them come up with even better lesson plans and approaches.
For example, several software providers offer mathematics packages to help teachers assess the current level of their students’
understanding, group students according to learning needs, and suggest lesson plans, materials, and problem sets for each group.
In other subjects, collaboration platforms enable teachers to search and find relevant materials posted by other teachers or administrators.
Technology has the least potential to save teacher time in areas where teachers are directly engaging with students: direct instruction and engagement, coaching and advisement, and behavioral-, social-, and emotional-skill development.
It is worth pausing here for a moment to note that we are not denying that technology will change the student experience of learning, although we would recommend caution and measured expectations.
Integrating effective software that links to student-learning goals within the curriculum—and training teachers on how to adapt to it—is difficult.
Instead of teaching a concept in the classroom and then having students go home to practice it, they assign self-paced videos as homework to give the basic instruction and then have students practice in the classroom, where the teacher can provide support and fill gaps in understanding.
Technology has already helped here—for example, computer grading of multiple-choice questions was possible long before AI and is particularly penetrated in math instruction.
Advances in natural-language processing make it possible for computers to assess and give detailed, formative feedback across long-form answers in all subject areas.
For example, writing software can look at trends in writing across multiple essays to provide targeted student feedback that teachers can review and tailor.
Automation could reduce the amount of time teachers spend on administrative responsibilities—down from five to just three hours per week.
Some of this time, hopefully, will be given back to teachers themselves—to spend time with their families and their communities—thus increasing the attractiveness of teaching as a profession.
While 60 percent of the teachers surveyed believed that their feedback was personalized to each student, only 44 percent of the students surveyed felt the same way.
Additional time can also help support social–emotional learning and the development of the 21st-century skillsthat will be necessary to thrive in an increasingly automated workplace.
It will enable teachers to foster one-on-one relationships with students, encourage self-regulation and perseverance, and help students collaborate with each other.
Research shows that strong relationships with teachers promote student learning and well-being, especially for students from low-income families.
International comparative studies show that high-performing school systems double down on peer coaching and collaborative lesson planning.
For example, the leerkRACHT Foundationin the Netherlands has introduced peer collaboration into 10 percent of Dutch schools, with 80 percent of teachers reporting improvement in student learning.
It will require commitment across a broad range of stakeholders, including governments, school leaders, technology companies, and, of course, teachers and learners themselves.
Four imperatives stand out as schools move to adopt technology wisely: target investment, start with easy solutions, share what is working, and build teacher and school-leader capacity to harness technology effectively.
The schools that are currently best in applying technology to save teacher time have often been able to access more funding than the average school.
As investment increases, it will be critical to target it to the areas that can most effectively save teacher time and improve student outcomes (rather than to flashy but ineffective hardware).
Proven technology that can replace simple administrative tasks or simple evaluative tools for formative testing can immediately provide teachers with respite, whetting their appetite for more holistic solutions.
Part of the problems that schools face today is the myriad of competing solutions, some of which are fantastic, but many of which promise great things but deliver little.
Neutral arbiters bringing objective and rigorous performance data, similar to the service that EdReports.org provides on curriculum, are necessary in the education-technology space.
Finally, building the capacity of teachers and school leaders to harness technology effectively will ensure maximum gains in not only saving teacher time but also improving student outcomes.
Districts and schools need to balance introducing new technologies with fully integrating existing ones into the curriculum and teachers’
Our review of relevant evidence shows that AI may act as an enabler on 134 targets (79%) across all SDGs, generally through a technological improvement, which may allow to overcome certain present limitations.
For instance, in SDG 1 on no poverty, SDG 4 on quality education, SDG 6 on clean water and sanitation, SDG 7 on affordable and clean energy, and SDG 11 on sustainable cities, AI may act as an enabler for all the targets by supporting the provision of food, health, water, and energy services to the population.
For example, AI can enable smart and low-carbon cities encompassing a range of interconnected technologies such as electrical autonomous vehicles and smart appliances that can enable demand response in the electricity sector13,14 (with benefits across SDGs 7, 11, and 13 on climate action).
Besides the fact that the human brain consumes much less energy than what is used to train AI models, the available knowledge introduced in the model (see, for instance, physics-informed deep learning18) does not need to be learnt through data-intensive training, a fact that may significantly reduce the associated energy consumption.
The term “big nudging” has emerged to represent using big data and AI to exploit psychological weaknesses to steer decisions—creating problems such as damaging social cohesion, democratic principles, and even human rights21.
It is also important to note that AI technology is unevenly distributed: for instance, complex AI-enhanced agricultural equipment may not be accessible to small farmers and thus produce an increased gap with respect to larger producers in more developed economies23, consequently inhibiting the achievement of some targets of SDG 2 on zero hunger.
There is another important shortcoming of AI in the context of SDG 5 on gender equality: there is insufficient research assessing the potential impact of technologies such as smart algorithms, image recognition, or reinforced learning on discrimination against women and minorities.
In the context of the Economy group of SDGs, if future markets rely heavily on data analysis and these resources are not equally available in low- and middle- income countries, the economical gap may be significantly increased due to the newly introduced inequalities30,31 significantly impacting SDGs 8 (decent work and economic growth), 9 (industry, innovation and infrastructure), and 10 (reduced inequalities).
By replacing old jobs with ones requiring more skills, technology disproportionately rewards the educated: since the mid 1970s, the salaries in the United States (US) salaries rose about 25% for those with graduate degrees, while the average high-school dropout took a 30% pay cut.
Such transfer of revenue from workers to investors helps explain why, even though the combined revenues of Detroit's “Big 3” (GM, Ford, and Chrysler) in 1990 were almost identical to those of Silicon Valley's “Big 3” (Google, Apple, and Facebook) in 2014, the latter had 9 times fewer employees and were worth 30 times more on the stock market32.
According to Mohamadi et al.39, neural networks and objective-oriented techniques can be used to improve the classification of vegetation cover types based on satellite images, with the possibility of processing large amounts of images in a relatively short time.
These AI techniques can help to identify desertification trends over large areas, information that is relevant for environmental planning, decision-making, and management to avoid further desertification, or help reverse trends by identifying the major drivers.
Furthermore, despite the many examples of how AI is increasingly applied to improve biodiversity monitoring and conservation40, it can be conjectured that an increased access to AI-related information of ecosystems may drive over-exploitation of resources, although such misuse has so far not been sufficiently documented.
Although accounting for the type of evidence has a relatively small effect on the positive impacts (we see a reduction of positively affected targets from 79% to 71%), we observe a more significant reduction (from 35% to 23%) in the targets with negative impact of AI.
In general, the fact that the evidence on interlinkages between AI and the large majority of targets is not based on tailored analyses and tools to refer to that particular issue provides a strong rationale to address a number of research gaps, which are identified and listed in the section below.
- On 10. april 2021
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