Education 2035 is an initiative that has at its foundation a core values statement (below) that guides all activity under its umbrella. It has as its goal in stage one, the imagining and strategic planning for the creation and application of artificial intelligence (AI) in the area of teaching and learning on the MSU campus by the year 2035. It was named such with the clear understanding that AI is already here and being used on campus in a variety of basic forms, yet with a more distant horizon to force participants to look beyond the next 1-3 years. In this way, faculty can practice educated, creative dreaming to identify current and anticipate future pedagogical challenges as well as the learning needs of the students of today and tomorrow in which AI might be helpful. This conversation will allow us to advocate with on and off-campus developers for the articulated faculty-led goals and design informed by student user-input.
Education Core Values Statement (as of March 4, 2019)**
Note: This text was largely written by Sonja Fritzsche and Andrew Christlieb based on conversations at a series of workshops and lunches during Fall 2018 with faculty from around the institution who have an interest in thinking about the values we must embody as we consider the technical futures of education.
The MSU Education 2035 initiative holds that technology and AI-enhanced learning must be adopted primarily for education improvement and not as a dollar-saving opportunity. When done well these are not inexpensive tools.
During fall 2018, we met three times to discuss a core values statement. The various participants found the following four values and subcategories/questions to be the guiding principles for successful technology adoption on campus in the area of teaching and learning. Only in this way will we be able to think critically around solving real problems today and into the future, rather than just streamlining and improving teaching. All activity of the Education 2035 initiative embodies these values.
Community – conversation, collaboration, shared decision making from community teams/brains that include faculty, students, staff, and administrators.
Diversity, Equity, and Inclusion – diversity of users reflected in the diversity of work groups, open source/open access, algorithmic literary, digital literacy, continual recognition of and measures taken to combat implicit data bias that reinforce existing cultural power structures.
Transparency – student and faculty data usage ethics, data privacy rights, a culture of data usage consent, data implicit bias, decision making and policies, transparency of data, algorithm, process.
- Data collection (D2L issues?): what gets collected and how long is it stored?
- Data ownership: who owns it?
- Data control: who gets control? How can students edit/adjudicate?
- Data access: who sees what, how is the data displayed, how do I ask for it?
- What happens when the company is sold?
- Data use (D2L): how does D2L use the data, how does MSU use the data, how to instructors use the data, how is any of this communicated to the students?
- Data communication: how is data shown?
Accountability – equity audit process, development of a university code of ethics for data ownership, data ethics MSU data privacy statement and syllabus information, assessment of compliance with first three values (Community, DEI, and Transparency), tools must solve real pedagogical problems, not just innovate for innovation’s sake.