some thoughts on emergent technology and the future of education

We often en­vi­sion the fu­ture of tech­nol­o­gy by pro­ject­ing today’s so­ci­ety for­ward, rather than con­sid­er­ing how fun­da­men­tal­ly dif­fer­ent it might be­come. This ten­den­cy can lead to amus­ing im­ages of hov­er­boards, like those seen in Back to the Fu­ture, or de­pic­tions of fu­tur­is­tic set­tings with out­dat­ed tech­nol­o­gy at the core, as in early Star Trek.

We’re at the edge of an­oth­er one of these shifts, and I worry we’re mak­ing the same mis­take — not being bold enough about how dif­fer­ent things might ac­tu­al­ly get. My up­bring­ing was marked by the in­ter­net rev­o­lu­tion­iz­ing com­mu­ni­ca­tion and in­for­ma­tion dis­tri­b­u­tion, and my gen­er­a­tion had to learn a fun­da­men­tal­ly new tech­nol­o­gy un­like any­thing be­fore it, along with ad­vances in com­put­ing, that re­shaped the world. The next gen­er­a­tion is going to have to learn how to in­ter­act and use emerg­ing tech­nolo­gies, such as AI, ro­bot­ics, au­toma­tion, en­er­gy gen­er­a­tion, and, I would add, VR and AR, in much the same way. These emerg­ing tech­nolo­gies are ex­pect­ed to drive a structural labor market churn of 22% by 2030, mean­ing near­ly a quar­ter of the labor mar­ket will look noth­ing like it does now — and that’s by 2030.

There is an op­ti­mistic ar­gu­ment that the jobs that are avail­able to the next gen­er­a­tion will sim­ply change, but there is also the more pes­simistic view that the avail­abil­i­ty of jobs may de­crease as pro­duc­tiv­i­ty be­comes con­cen­trat­ed in fewer hands amid in­creas­ing com­pe­ti­tion over fi­nite re­sources (if you ever want to make your­self de­pressed for an af­ter­noon, I en­cour­age you to go read Dr. Tim Morgan’s work over at Surplus Energy Economics). While I lean to­ward long-term op­ti­mism, I can’t help but fore­see im­mense short-term tur­bu­lence as we grap­ple with re­struc­tur­ing labor mar­kets, or even re­think­ing the eco­nom­ic struc­ture of so­ci­ety. Emerg­ing tech­nolo­gies will like­ly con­sol­i­date ca­reer path­ways into a few, spe­cial­ized, high-skill po­si­tions, much like how cor­po­rate and tech power has be­come con­cen­trat­ed in a few dom­i­nant en­ti­ties. The white-col­lar cler­i­cal and ser­vice roles that once formed a large em­ploy­ment seg­ment will shrink, and not every­one will find com­pa­ra­bly paid work with­in their skill set. Mean­while, blue-col­lar work will con­tin­ue to be dis­placed as robotics installations replace an average of 1.6 manufacturing employees per machine, with many dis­placed work­ers un­able to reskill quick­ly enough to se­cure new op­por­tu­ni­ties.

If we view work­force de­vel­op­ment as one of ed­u­ca­tion’s so­ci­etal roles, then I think most school cur­ric­u­la world­wide are not pre­pared for this, and in­sti­tu­tion­al in­er­tia is caus­ing adap­ta­tion to move too slow­ly. While ed­u­ca­tion should fos­ter holis­tic growth (building agency, willfulness, and determination) we must also se­ri­ous­ly con­sid­er ac­cel­er­at­ing ca­reer spe­cial­iza­tion. Ear­li­er ex­po­sure to spe­cial­ized skills could help stu­dents nav­i­gate short-term eco­nom­ic up­heaval and com­pete in a job mar­ket that will ex­pect more from them soon­er. Tra­di­tion­al high school cur­ric­u­la should ac­cel­er­ate early spe­cial­iza­tion op­por­tu­ni­ties, such as cre­at­ing cours­es that de­vel­op a deep­er un­der­stand­ing of Ma­chine Learn­ing, Cy­ber­se­cu­ri­ty, Data Analy­sis, and Ro­bot­ics, along with micro-cre­den­tial­ing path­ways that allow stu­dents to gain rec­og­nized skills be­fore uni­ver­si­ty.

One ex­am­ple of an area that ed­u­ca­tion is mov­ing too slow­ly on is in con­sid­er­ing the changes oc­cur­ring in pro­gram­ming fields. The learn to code move­ment was dri­ven in schools as a for­mu­la for suc­cess and job mar­ket op­por­tu­ni­ties that are like­ly to not exist any­more in the short term. Just five years ago the fields were pop­u­lat­ed with peo­ple who thought their oc­cu­pa­tion­al po­si­tion made them spe­cial, unique, and in­dis­pens­able…until they weren’t. Salesforce has announced that it will not hire any new software engineers in 2025. Google reports that 25% of its new code is AI-generated. Mark Zuckerberg has openly stated his intent to automate coding jobs this year. While AI will not re­place the prob­lem-solv­ing ca­pa­bil­i­ties of ex­pe­ri­enced en­gi­neers, the de­mand for ju­nior de­vel­op­ers will plum­met as AI-as­sist­ed cod­ing tools con­tin­ue to im­prove. Com­put­ing cours­es in sec­ondary schools must evolve ac­cord­ing­ly, yet in­sti­tu­tions are slow to react. The IB Pro­gram of Stud­ies, for in­stance, still op­er­ates with out­dat­ed as­sump­tions about fu­ture job mar­kets and what skills stu­dents in these cours­es will need to be suc­cess­ful. Schools that im­ple­ment ac­cel­er­at­ed learn­ing pro­grams and real AI-in­te­grat­ed course­work will put their stu­dents ahead.

The teach­ing pro­fes­sion is not im­mune to AI-dri­ven dis­rup­tion. Per­haps not by 2030, but like­ly with­in my life­time. If you are an ed­u­ca­tor read­ing this (though the fol­low­ing ques­tion could eas­i­ly be mod­i­fied for other pro­fes­sions, please feel free), here’s the ques­tion I keep com­ing back to:

What does my school, or my class, ac­tu­al­ly offer that is so unique that it can not be dis­placed by an in­fi­nite­ly pa­tient, and much more broad­ly knowl­edge­able AI? What does it offer under how our so­ci­ety is cur­rent­ly struc­tured, and what does it offer if we com­plete­ly re­think the model of a tra­di­tion­al school­ing day as we know it?

There are a few com­mon coun­ter­ar­gu­ments I keep hear­ing, and I want to push back on each. For all of what fol­lows, as­sume the tools we have today will keep get­ting bet­ter — which, based on cur­rent tra­jec­to­ries, is near­ly cer­tain.

The most com­mon re­sponse I hear is that human con­nec­tion can’t be repli­cat­ed. But AI is al­ready show­ing promis­ing abil­i­ties in gen­er­at­ing em­pa­thet­ic, well-re­ceived re­spons­es. Research by Hatch and colleagues found that AI-generated content is rated highly by therapists, often outperforming human professionals in perceived empathy. While AI may cur­rent­ly lack true ther­a­peu­tic ef­fec­tive­ness (I gen­er­al­ly find re­spons­es from Chat­G­PT to be too agree­able), these are the weak­est ver­sions of these tools — fu­ture ad­vance­ments may eas­i­ly over­come these lim­i­ta­tions.

Then there’s per­son­al­ized learn­ing. AI tutors have demonstrated the ability to double learning efficiency in comparison to active learning classrooms, and had students feeling more engaged and motivated. In developing contexts, AI-powered after-school programs have resulted in learning gains equivalent to two years of education in just six weeks. These tools aren’t a re­place­ment for teach­ers today. But as they get more per­son­al­ized and more avail­able, they could out­per­form con­ven­tion­al meth­ods on both qual­i­ty and cost. How con­fi­dent are you in your abil­i­ty as an ed­u­ca­tor to out com­pete an in­fi­nite­ly pa­tient, much more knowl­edge­able, and per­fect­ly per­son­al­ized AI teacher?

As­sess­ment and grad­ing are an­oth­er area where the ground is shift­ing. Pre­vi­ous Au­to­mat­ed Essay Scor­ing (AES) sys­tems have already matched human graders in accuracy and consistency while eliminating fatigue and bias drift. Stud­ies have shown that human graders often have low inter-rater reliability, and even the same grader can be inconsistent across sessions. Technology driven grading tools offer quick feedback and are often perceived as more impartial or fairer than human teachers. Many of these tools were al­ready show­ing promise be­fore the cur­rent AI surge, and they will only han­dle more com­plex tasks across a wider range of con­tent areas from here.

Fi­nal­ly, there’s the as­sump­tion that ex­tracur­ric­u­lars re­quire the tra­di­tion­al school struc­ture — a six- to seven-hour school day fol­lowed by after-school ac­tiv­i­ties. But al­ter­na­tive mod­els al­ready exist. South Alberta Hockey Academy runs four-hour academic days paired with four-hour hockey training sessions. They have been suc­cess­ful­ly run­ning this pro­gram for years and, in my ex­pe­ri­ence, work­ing along­side peo­ple who came out of this pro­gram at my Uni­ver­si­ty were some of the most in­ter­est­ing and tal­ent­ed peo­ple in my class­es. Imag­ine a fu­ture where AI-based ed­u­ca­tion en­ables stu­dents to com­plete aca­d­e­m­ic course­work ef­fi­cient­ly, free­ing time for ex­tracur­ric­u­lar pur­suits tai­lored to their spe­cif­ic in­ter­ests.

I strong­ly be­lieve that the first pres­ti­gious sec­ondary in­sti­tu­tion in each re­gion to em­brace a thought­ful emer­gent tech­nol­o­gy and AI-dri­ven ini­tia­tive to learn­ing, both in per­son and across dis­tances, where ex­pe­ri­enced teach­ers pri­mar­i­ly su­per­vise AI agents, will pull en­roll­ment from every tra­di­tion­al school in the area. If the pat­tern from other in­dus­tries holds, the con­sol­i­da­tion could be se­vere — most tra­di­tion­al school­ing struc­tures won’t sur­vive un­changed.

I am pur­pose­ful­ly being bold in my pre­dic­tions, and only time will tell on how many of these ideas be­come re­al­i­ty, but I cau­tion those who dis­miss these ideas out­right. They may be un­der­es­ti­mat­ing how dra­mat­i­cal­ly the fu­ture will di­verge from today’s ex­pec­ta­tions. Amara’s Law states:

“We tend to over­es­ti­mate the ef­fect of a tech­nol­o­gy in the short run and un­der­es­ti­mate the ef­fect in the long run.”

As bold as some of these pre­dic­tions may be, I fear I am still un­der­es­ti­mat­ing the long-term im­pact of AI on ed­u­ca­tion. Those who are pre­pared to move on these changes early will have a se­ri­ous edge. Con­sid­er NVIDIA’s rise. Over a decade ago, they positioned themselves as a leader in AI long before the technology became mainstream, and now, they dominate an industry almost entirely of their own creation.

The same prin­ci­ple ap­plies to ed­u­ca­tion. Schools and in­sti­tu­tions that proac­tive­ly, au­then­ti­cal­ly, embed emerg­ing tech­nolo­gies into their cur­ric­u­la will pro­duce stu­dents who can com­pete in the labor mar­ket that is com­ing. Too much en­er­gy is wast­ed on bu­reau­crat­ic de­bates over AI poli­cies rather than real tech­no­log­i­cal in­te­gra­tion¹¹ I am being intentionally strong armed with my usage of the word “wasted” here, because I do believe there are discussions to be had. One thing I always ask people is what actual measurable value does something like an AI policy bring? What problem does it actually solve? ²² Shortly after publishing this piece I decided to set OpenAI’s Deep Research to the job of taking a look at the efficacy of technology policy in schools. I haven’t gone into a deep look at it’s sources yet, but it claims at the top that hardware policy is effective, while software policy generally doesn’t seem to do much (and guess what category AI would fall under). . I wit­nessed sim­i­lar waste when schools at­tempt­ed to ban Wikipedia for re­search when I was a stu­dent—as we just used it any­way. The same is hap­pen­ing with AI. In­stead of fo­cus­ing on re­stric­tive poli­cies, we should in­vest in em­bed­ding AI into ed­u­ca­tion in ways that build dig­i­tal lit­er­a­cy and align with real-world tech­no­log­i­cal usage.

While I have met a few in­di­vid­ual ed­u­ca­tors who un­der­stand the ur­gency of these shifts, I have yet to see a shift across the in­dus­try that rec­og­nizes how much has to change. I worry my field is mov­ing too slow­ly. Whether it catch­es up — and what “catch­ing up” even looks like — is some­thing I keep turn­ing over with­out a clean an­swer.

Footnotes

1. I am being in­ten­tion­al­ly strong armed with my usage of the word “wast­ed” here, be­cause I do be­lieve there are dis­cus­sions to be had. One thing I al­ways ask peo­ple is what ac­tu­al mea­sur­able value does some­thing like an AI pol­i­cy bring? What prob­lem does it ac­tu­al­ly solve? ↩︎

2. Short­ly after pub­lish­ing this piece I de­cid­ed to set Ope­nAI’s Deep Re­search to the job of tak­ing a look at the ef­fi­ca­cy of tech­nol­o­gy pol­i­cy in schools. I haven’t gone into a deep look at it’s sources yet, but it claims at the top that hardware policy is effective, while software policy generally doesn’t seem to do much (and guess what cat­e­go­ry AI would fall under). ↩︎