In experiments at six public universities, students assigned randomly to statistics courses that relied heavily on “machine-guided learning” software — with reduced face time with instructors — did just as well, in less time, as their counterparts in traditional, instructor-centric versions of the courses. This largely held true regardless of the race, gender, age, enrollment status and family background of the students.

The study comes at a time when “smart” teaching software is being increasingly included in conversations about redrawing the economics of higher education. Recent investments by high-profile universities in “massively open online courses,” or MOOCs, has elevated the notion that technology has reached a tipping point: with the right design, an online education platform, under the direction of a single professor, might be capable of delivering meaningful education to hundreds of thousands of students at once.

The new research from the nonprofit organization Ithaka was seeking to prove the viability of a less expansive application of “machine-guided learning” than the new MOOCs are attempting — though one that nevertheless could have real implications for the costs of higher education.