Professor & Canada Research Chair in Innovative Learning and Technology at Royal Roads University

Bots, AI, & Education update #3

Posted on March 23rd, by George Veletsianos in teacherbots. Comments Off on Bots, AI, & Education update #3

Today’s rough set of notes that focus on teacherbots and artificial intelligence in education

  • Chatbots: One of the technologies that’s mesmerized silicon valley
  • Humans have long promised future lives enhanced by machines
  • Many proponents highlight the qualities of bots vis-a-vis teachers
    • personal
    • personalized
    • monitoring & nudging
    • can give reliable feedback
    • don’t get tired
    • etc etc
  • Knewton: Algos to complement and support teacher (sidenote: as if anyone will be forthright about aiming to replace teachers… except perhaps this book that playfully states that “coaches (once called teachers)” will cooperate with AI)
  • Genetics with Jean: bots with affect-sensing functionality, ie software that detects students’ affective states and responds accordingly
  • Driveleress Ed-Tech: Robots aren’t going to march in for jobs; it’s the corporations and the systems that support them that enable that to happen.

Bots, AI, & Education update #2

Posted on March 23rd, by George Veletsianos in teacherbots. Comments Off on Bots, AI, & Education update #2

Yesterday’s rough set of notes that focus on teacherbots and artificial intelligence in education

  • Notable critiques of Big Data, data analytics, and algorithmic culture (e.g., boyd & Crawford, 2012; Tufecki, 2014 & recent critiques of YouTube’s recommendation algorithm as well as Caulfield’s demonstration of polarization on Pinterest). These rarely show up in discussions around bots and AI in education, critiques of learning analytics and big data (e.g., Selwyn 2014; Williamson, 2015) are generally applicable to the technologies that enable bots to do what they do (e.g., Watters, 2015).
  • Complexity of machine learning algorithms means that even their developers are at times unsure as to how said algorithms arrive at particular conclusions
  • Ethics are rarely an area of focus in instructional design and technology (Gray & Boling, 2016)  – and related edtech-focused areas. In designing bots where should we turn for moral guidance? Who are such systems benefiting? Whose interests are served? If we can’t accurately predict how bots may make decisions when interacting with students (see bullet point above), how will we ensure that moral values are embedded in the design of such algorithms? Whose moral values in a tech industry that’s mired with biases, lacks broad representation, and rarely heeds user feedback (e.g., women repeatedly highlighting the harassment they experience on Twitter for the past 5 or so years, with Twitter taking few, if any, steps to curtail it)?

Bots, AI, & Education update #1

Posted on March 21st, by George Veletsianos in teacherbots. 2 comments

A rough set of notes from today that focus on teacherbots and artificial intelligence in education

  • Bots in education bring together many technologies & ideas including, but not limited to artificial intelligence, data analytics, speech-recognition technologies, personalized learning, algorithms, recommendation engines, learning design, and human-computer interaction.
    • They seek to serve many roles (content curation, advising, assessment, etc)
  • Many note the potential that exists in developing better algorithms for personalized learning. Such algos are endemic in the design of AI and bots
    • Concerns: Black box algorithms, data do not fully capture learning & may lead to biased outcomes & processes
  • Downes sees the crux of the matter as What AI can currently do vs. What AI will be able to do
    • This is an issue with every new technology and the promises of its creators
    • Anticipated future impact features prominently in claims surrounding impact of tech in edu
  • Maha Bali argues that AI work misunderstands what teachers do in the classroom
    • Yet, in a number of projects we see classroom observations as being used to inform the design of AI systems
  • “AI can free time for teachers to do X” is an oft-repeated claim of AI/bot proponents. This claim often notes that AI will free teachers from mundane tasks and enable them to focus on those that matter. We see this in Jill Watson, in talks from IBM regarding Watson applications to education, but also in earlier attempts to integrate AI, bots, and pedagogical agents in education (e.g., 1960s, 1980s). Donald Clark reiterates this when he argues that teachers should “welcome something that takes away all the admin and pain.” See update* below.
  • Another oft-repeated claim is that AI & bots will work with teachers, not replace them
  • At times this argument is convincing. At other times, it seems dubious (e.g., when made in instances where proponents ask readers/audience to imagine a future where every child could have instant access to [insert amazing instructor here])
  • Predictions regarding the impact of bots and AI abound (of course). There’s too many to list here, but here’s one example
  • Why a robot-filled education future may not be as scary as you think” argues that concerns around robots in education are to be expected. The article claims that people are “hard-wired” to perceive “newness as danger” as it seeks to explain away concerns by noting that education, broadly speaking, avoids change. There’s no recognition anywhere in the article that (a) education is, and has always been, in a constant state of change, and (b) edtech has always been an optimistic endeavour, so much so that its blind orthodoxy has been detrimental to its goal of improving education.



From Meet the mind-reading robo tutor in the sky:

And underpaid, time-stressed teachers don’t necessarily have the time to personalize every lesson or drill deep into what each child is struggling with.

Enter the omniscient, cloud-based robo tutor.

“We think of it like a robot tutor in the sky that can semi-read your mind and figure out what your strengths and weaknesses are, down to the percentile,” says Jose Ferreira, the founder and CEO of ed-tech company Knewton.”

Tri-council guidance on using online public data in research

Posted on March 9th, by George Veletsianos in emerging technologies, NPS, open, scholarship. Comments Off on Tri-council guidance on using online public data in research

I am often asked whether there are Canadian ethics guidelines on the use of online public data in research. The  relevant section from the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans is provided below. I believe that researchers should take further steps to protect privacy and confidentiality pertaining to public data, but with regards to accessing and using public online data, this is a start.

A sample project to which these guidelines may apply is the following:  The researcher will collect and analyze Twitter profiles and postings of higher education stakeholders (e.g., faculty, researchers, administrators) and institutional offices (e.g., institutional Twitter accounts). This research will use exclusively publicly available information. Private Twitter accounts (ie those that are not public and involve an expectation of privacy) will be excluded from the research. The purposes of the research is to gain a better understanding of Twitter metrics, practices, and use/participation.


=== Begin relevant Tricouncil guidance ===

Retrieved on December 12 2014 from

REB review is also not required where research uses exclusively publicly available information that may contain identifiable information, and for which there is no reasonable expectation of privacy. For example, identifiable information may be disseminated in the public domain through print or electronic publications; film, audio or digital recordings; press accounts; official publications of private or public institutions; artistic installations, exhibitions or literary events freely open to the public; or publications accessible in public libraries. Research that is non-intrusive, and does not involve direct interaction between the researcher and individuals through the Internet, also does not require REB review. Cyber-material such as documents, records, performances, online archival materials or published third party interviews to which the public is given uncontrolled access on the Internet for which there is no expectation of privacy is considered to be publicly available information.

Exemption from REB review is based on the information being accessible in the public domain, and that the individuals to whom the information refers have no reasonable expectation of privacy. Information contained in publicly accessible material may, however, be subject to copyright and/or intellectual property rights protections or dissemination restrictions imposed by the legal entity controlling the information.

However, there are situations where REB review is required.

There are publicly accessible digital sites where there is a reasonable expectation of privacy. When accessing identifiable information in publicly accessible digital sites, such as Internet chat rooms, and self-help groups with restricted membership, the privacy expectation of contributors of these sites is much higher. Researchers shall submit their proposal for REB review (see Article 10.3).

Where data linkage of different sources of publicly available information is involved, it could give rise to new forms of identifiable information that would raise issues of privacy and confidentiality when used in research, and would therefore require REB review (see Article 5.7).

When in doubt about the applicability of this article to their research, researchers should consult their REBs.

=== End relevant Tricouncil guidance ===