Although Artificial Intelligence (AI) and Learning Analytics (LA) have shown their potential in Education, stakeholders’ agency seems to be threatened. On the other hand, multiple issues regarding FATE (Fairness, Accountability, Transparency and Ethics) have been raised when AI or LA-based solutions are designed and implemented. These issues have been especially acute since the emergence of Large Language Models and Generative AI.   This talk discusses the quest for an optimal balance between human and computational agents, when LA tools and services are employed in a Technology Enhanced Learning (TEL) ecosystem. Through the discussion of relevant conceptual models and examples, it argues for Human-Centered Learning Analytics (HCLA) and Human-Centered Artificial Intelligence (HCAI) approaches, where agency and FATE principles are essential design parameters.   The talk focuses especially on LA/AI solutions that may position teachers as designers of effective interventions and orchestration actions. Selected Human-Centered Design (HCD) principles are discussed and illustrated, and directions for future research and development are formulated to overcome the main obstacles for adoption of human-centered approaches for LA and AI in education.

1. Human-Centered Learning Analytics and
Artificial Intelligence in Education: Human-
centered design approaches towards increased
teacher agency and compliance to FATE
principles
Prof. Yannis Dimitriadis
GSIC/EMIC group
University of Valladolid, Spain
SALTISE
February 27, 2024

2. Lots of acknowledgements
2

3. Our main goal
What Artificial Intelligence (AI)
and Learning Analytics (LA) may
contribute to Education?
Why Human Agency is needed so
that AI/LA may comply with FATE
(Fairness, Accountability, Trust,
Ethics) principles? 3

4. Artificial Intelligence
as an umbrella term
4
Regona, Massimo & Yigitcanlar, Tan & Xia, Bo & Li, R.Y.M. (2022). Opportunities and adoption challenges of AI in the
construction industry: A PRISMA review. Journal of Open Innovation Technology Market and Complexity, 8(45).
https://doi.org/10.3390/joitmc8010045
Any computational method that is made to act independently
towards a goal based on inferences from theory or patterns in data.
Friedman, L., Blair Black, N., Walker, E., & Roschelle, J. (November 8, 2021) Safe AI in education needs you. Association of
Computing Machinery blog, https://cacm.acm.org/blogs/blog-cacm/256657-safe-ai-in-education-needs-you/fulltext

5. Artificial Intelligence
in Education
5
n Generic AI systems adopted in Education vs.
tools designed for teaching/learning
n Innovation typically serves for
– Personalized learning based on models
– Supporting teachers in assessing and advising
n Generative AI models may serve for “almost
everything”
– Offload labor intensive tasks
– Scaffold learning design or student learning (?)

6. Intelligent Tutoring Systems
(Just-in-time, individualized, on-demand advice)
6
V. Aleven, B. Mclaren, I. Roll, K. Koedinger (2006), Toward meta-cognitive tutoring: a model of help seeking with a Cognitive Tutor,
Int. J. Artif. Intell. Educ., 16 (2), pp. 101-128
.
How is the COGNITIVE SKILL MODEL developed?
How is PERSONALIZED ASSESSMENT/FEEDBACK generated?
How are HINTS and GLOSSARY provided?

7. Generative AI
(Outline of existing essay)
7
Hodges, C.B., Kirschner, P.A. (2024) Innovation of Instructional Design and Assessment in the Age of Generative Artificial
Intelligence. TechTrends 68, 195–199. https://doi.org/10.1007/s11528-023-00926-x.
Why should a STUDENT TRUST this outline?
Is there a need for CRITICAL ASSESSMENT of the product?
How has the outline been GENERATED?

8. Learning Analytics (LA)
in Technology Enhanced Learning (TEL)
Learning Analytics
“measurement, collection, analysis and reporting of data
about learners and their contexts, for purposes of
understanding and optimizing learning and the
environments in which it occurs”
n Most R&D - Innovation has been devoted to
– Mining patterns
– Deriving predictive models
– Providing dashboards
8

9. Predictive models with LA
(At-risk students)
9
Herodotou, C.; Hlosta, M.; Boroowa, Avinash; R., Bart; Zdrahal, Z. and Mangafa, C. (2019). Empowering online
teachers through predictive learning analytics. British Journal of Educational Technology, 50(6) pp. 3064–3079.
How does the predictive model work and how it was trained?
How were the data collected for this prediction model?
Who was involved in its design and who can use the data?

10. Pattern mining using LA
(Detection of learning strategies)
10
J. B. J. Huang, A. Y. Q. Huang, O. H. T. Lu and S. J. H. Yang, "Exploring Learning Strategies by Sequence Clustering
and Analysing their Correlation with Student's Engagement and Learning Outcome," 2021 International Conference
on Advanced Learning Technologies (ICALT), 2021, pp. 360-362, doi: 10.1109/ICALT52272.2021.00115.
How are the proxies for strategies DEFINED AND COMPUTED?
Who can interpret this data and how?
Is there any student bias regarding these strategies?

11. LA-based dashboards
(Monitoring and sense-making)
11
S. Charleer, A. V. Moere, J. Klerkx, K. Verbert and T. De Laet, "Learning Analytics Dashboards to Support Adviser-
Student Dialogue," in IEEE Transactions on Learning Technologies, vol. 11, no. 3, pp. 389-399, 1 July-Sept. 2018,
doi: 10.1109/TLT.2017.2720670
How effective is sense-making out of those dashboards?
Do teachers-students need to improve their data literacy?
Can we compensate the sense-making workload?

12. Smart Learning Environments
(Personalized recommendations-resources)
12
S. Serrano-Iglesias, E. Gómez-Sánchez, M. L. Bote-Lorenzo, G. Vega-Gorgojo, A. Ruiz-Calleja and J. I. Asensio-Pérez
(2021) "From Informal to Formal: Connecting Learning Experiences in Smart Learning Environments," 2021 International
Conference on Advanced Learning Technologies (ICALT), pp. 363-364, doi: 10.1109/ICALT52272.2021.00116.
How is the student model built?
Do teachers/students get involved in the reaction scripts?
What about privacy in informal learning settings?

13. Two dilemmas on Agency (I)
Dilemma 1: Learning Analytics (LA) may be
helpful when embedded in Technology-Enhanced
Learning (TEL) contexts. They are typically
designed by researchers and developers, that best
know about efficiency and effectiveness. But
existing LA solutions mostly ignore teachers as
orchestrators (designers and enactors).
What about teachers’ agency?
13

14. Two dilemmas on Agency (II)
Dilemma 2: AI agents that are using LA or GenAI
agents may support student productivity and
eventually maximize students’ learning but how can
they be transparent, trustful, responsible or ethical?
What about students’ agency?
14

15. FATE AI principles
n No consensus on definitions and proxies
– Fairness, Accountability, Transparency & Ethics
n European Union Principles for Trustworthy AI
– Human Autonomy, no Harm, Fairness, Explicability
15
HLEG-AI (High-Level Expert Group on Artificial Intelligence) (2019), “Ethics Guidelines for Trustworthy AI: Requirements
of Trustworthy AI,” Available: https://ec.europa.eu/futurium/en/ai-alliance-consultation/guidelines/1

16. AI and the future of learning
16
Roschelle, J., Lester, J. & Fusco, J. (Eds.) (2020). AI and the future of learning: Expert panel
Report. Digital Promise. https://circls.org/reports/ai-report.
1. Investigate AI Designs for an Expanded Range of Learning Scenarios
2. Develop AI Systems that Assist Teachers and Improve Teaching
3. Intensify and Expand Research on AI for Assessment of Learning
4. Accelerate Development of Human-Centered or Responsible AI
5. Develop Stronger Policies for Ethics and Equity
6. Inform and Involve Educational Policy Makers and Practitioners.
7. Strengthen the Overall AI and Education Ecosystem
Seven recommendations from US expert panel

17. FATE AI principles
n Fairness
– Lack of bias in training data
n Accountability
– Compliance with regulations, policies, standards
– Responsibility of users, algorithms, designers
n Transparency
– Descriptions of Inner workings, Input/Output, Inner
workings/Output
– System built on scrutinizable open sources
n Ethics
– Privacy, Well-being, Integrity, Environmental Impact
17

18. Highlights of the rest of this talk
n Student and teacher agency: Central elements to
FATE principles for LA/AI
n Human-centeredness (HC) and teachers as
designers and orchestrators: Critical conditions
n SALTISE approach: Compliance to the above 
n Human-AI collaboration: The path to follow
n Some suggestions for HCLA/HCAI
n A few examples from my own research
n Promises, limitations, take-home messages
18

19. The second goal
Why and how Human-Centered
Approaches
may account for teacher/learner
agency and
compliance to FATE principles?
20

20. A definition of teachers’ agency
21
Priestley, M., Biesta, G., & Robinson, S. (2015). Teacher agency: What is it and why does it matter? In R. Kneyber & J. Evers (Eds.), Flip
the System: Changing Education from the Ground Up (pp. 134–148). Routledge. https://doi.org/10.4324/9781315678573 (adapted)
Agency entails the capacity of actors to make practical and normative judgments
among alternative possible trajectories of action, in response to the emerging
demands, dilemmas, and ambiguities of presently evolving situations

21. A socio-cultural perspective of
professional agency
22
Eteläpelto, A., Vähäsantanen, K., Hökkä, P., & Paloniemi, S. (2013). What is agency? Conceptualizing professional
agency at work. Educational Research Review, 10, 45–65. https://doi.org/10.1016/j.edurev.2013.05.001 (adapted)

22. Teachers as producers and shapers
23
Jenkins, G. (2020). Teacher agency: the effects of active and passive responses to curriculum change. Australian
Educational Researcher, 47(1), 167–181. https://doi.org/10.1007/s13384-019-00334-2

23. Digital agency
24
Passey, D., Shonfeld, M., Appleby, L., Judge, M., Saito, T., & Smits, A. (2018). Digital Agency: Empowering Equity in
and through Education. Technology, Knowledge and Learning, 23(3), 425–439. https://doi.org/10.1007/s10758-018-
9384-x (adapted)
Control over and adapt to …
Be proactive producers
Be aware of the data
Decide what data is relevant

24. Human-centered design landscape
25
Sanders, E. B. N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. Co-design, 4(1), 5-18.

25. From User-Centered Design to Co-Design
26
User-centred design Co-creation (co-design)
User
Researcher
Designer
Sanders, E. B. N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. Co-design, 4(1), 5-18.

26. Human-Centered Learning Analytics
27
What is human-centeredness
in Learning Analytics then?

27. Human-Centered Learning Analytics
28
Human centeredness has been identified in other
fields as a characteristic of systems that have been
carefully designed by:
• identifying the critical stakeholders
• their relationships
• the contexts in which those systems will function
.

28. Human-Centered Learning Analytics
29
HCD should involve:
Inclusion via stakeholder participation in the design process
+
Empathic experiences (particularly when making design
decisions).
Giacomin, J. (2014). What is human centred design? The Design Journal,
17(4), 606–623. https://doi.org/10.2752/175630614X140561854801.

29. Human-Centered Learning Analytics
Human-centered design considered harmful…
“Most items in the world have been designed without the
benefit of user studies and the methods of Human-Centered
Design. Yet they do quite well.”
What Adapts? Technology or People?
Don Norman proposes stronger focus on tasks and activities
Norman, D. A. (2005). Human-centered design considered harmful. interactions, 12(4), 14-19.

30. Human-Centered Learning Analytics
the human centered (not centric)
All the human factors,
social factors and
technology factors
interact together under the
human activity umbrella.

31. Augmented teacher
(Human-AI complementarity)
32
Holstein, K., Aleven, V., Rummel, N. (2020). A Conceptual Framework for Human–AI Hybrid Adaptivity in Education.
In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Artificial Intelligence in Education. AIED
2020. Lecture Notes in Computer Science(), vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-
7_20
n Augmentation
– Complementary strengths and weaknesses
– Improvement (co-learning) over time
n Goals
– Optimized objective functions + design
decisions
n Perceptions
– Sense, attention, interpretation

32. Augmented teacher
(Human-AI complementarity)
33
Holstein, K., Aleven, V., Rummel, N. (2020). A Conceptual Framework for Human–AI Hybrid Adaptivity in Education.
In: Bittencourt, I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds) Artificial Intelligence in Education. AIED
2020. Lecture Notes in Computer Science(), vol 12163. Springer, Cham. https://doi.org/10.1007/978-3-030-52237-
7_20
n Actions
– Action space, scalability and capacity
n Decisions
– Link perception and action – make effective
pedagogical interventions
n Timing and granularity
– e.g., adaptation by teachers through LA
dashboards, during learn time, regarding a
task

33. Augmented teacher
(Human-centered approach)
34
Holstein, K., & Aleven, V. (2022). Designing for human-AI complementarity in K-12 education. }, ArXiv,
abs/2104.01266
Echevarría, V. Yang, K., Lawrence, L., Rummel, N., Aleven V., (2020). Exploring Human–AI Control Over Dynamic
Transitions Between Individual and Collaborative Learning, In Proceedings of ECTEL 2020
“
n Lumilo project (CMU) on human-AI
partnership in real-world K-12 education
n Co-orchestration (ITS and teachers) of
transitions from individual to group
activities
n Adoption of participatory (human-
centered) approach to design and
development lifecycle

34. Levels of human-centeredness
35
Smuha N.A. (2023). “Pitfalls and pathways for trustworthy Artificial Intelligence in education” in The Ethics of Artificial
Intelligence in Education Practices, Challenges, and Debates, W. Holmes, K. Porayska-Pomsta (Eds). Taylor and
Francis.
n Human in command
– Oversee when and how to use AI/LA
n Human on the loop
– Participate in design and operation
n Human in the loop
– Get involved in every lifecycle phase

35. Human-Centeredness in MMLA-AIED
36
Cukurova, M. (2022). “Multimodal Learning Analytics in Real-world Practice: A Bridge Too Far?”, Webinar at Spanish
Network of Learning Analytics (SNOLA), May 2022. https://snola.es/2022/05/03/webinar-multimodal-learning-
analytics-in-real-world-practice-a-bridge-too-far-mutlu-cukurova/

36. Some elements to consider
n LA solutions were eventually pushed by new
technological (Data and AI) affordances
n Teachers as designers were not always
considered in complex real-world TEL spaces
n The hybrid AI-human models and their trade-
offs were not fully studied
n Learning theories have not been used
extensively while designing LA solutions
37

37. The complexity of TEL ecosystems
(Hybrid Learning Spaces)
38
Gil, Mor, Dimitriadis & Köppe (2022): Hybrid Learning Spaces, Springer https://doi.org/10.1007/978-3-030-88520-5

38. Design and orchestration
39
Prieto, L. P., Y. Dimitriadis, J. I. Asensio-Pérez, C. K. Looi (2015). “Orchestration in learning technology
research: evaluation of a conceptual framework”. In: Research in Learning Technology 23.0
How to support teachers as designers and reduce/optimize their
orchestration load?

39. Teachers as designers
n Pedagogical knowledge
– Eventually embedded in tools
– Complements / cooperates with the tacit and
explicit knowledge of the teachers
n Teachers
– Are and can serve as designers
– Should participate in the design and
orchestration of the teaching and learning
processes
40
Kali, McKenney & Sagy (2015), Instructional Science: An International Journal of the Learning Sciences, 4(2) 173-179 Mar 2015

40. The METIS ILDE
LD and orchestration tools
• Villasclaras-Fernández, Hernández-Leo, Asensio-Pérez & Dimitriadis (2013)
• Håklev, Faucon, Hadzilacos & Dillenbourg (2017) - Figure
• Laurillard, Kennedy, Charlton, Wild & Dimakopoulos (2018) - Figure

41. Balancing computer-human agents
42
Sharples, M. (2013). Shared Orchestration Within and Beyond the Classroom. Computers &
Education. 69. 504-506. 10.1016/j.compedu.2013.04.014.

42. Mirroring, Advising, Guiding through LA
43
Soller, A., Martínez-Monés, A., Jermann, P., Muehlenbrock, M. (2005) From Mirroring to Guiding:
A Review of the State of the Art Technology for Supporting Collaborative Learning International
Journal of Artificial Intelligence in Education (ijAIED). 15:261-290

43. A process model of LA use
44
Wise, A. F., & Jung, Y. (2019). Teaching with Analytics: Towards a Situated Model of
Instructional Decision-Making. Journal of Learning Analytics, 6(2), 53–69.
https://doi.org/10.18608/jla.2019.62.4

44. Checkpoint and process analytics
45
Bakharia, A., Corrin, L., de Barba, P, Kennedy, G., Gasevic, D., Moulder, R., Williams, D., Dawson, D., Lockyer, L.
(2016). A conceptual framework linking learning design with learning analytics, LAK ‘16, 2016 Pages 329–338
https://doi.org/10.1145/2883851.2883944

45. Mirroring and advising dashboards
46
A. van Leeuwen, N. Rummel (2020). Comparing teachers' use of mirroring and advising dashboards. In Proceedings of the Tenth
International Conference on Learning Analytics & Knowledge (LAK '20)
Teachers in the advising condition more often detected the problematic group,
needed less effort to do so, and were more confident of their decisions

46. Design LA for Actionability
n From sensemaking to actionable insights
n Design strategies for actionability
– Situate LA into student routines
– Consider plurality of actions
n Features of actionable analytics
– Timing of analytics that match timing of learning
– Suggestions individualized to students
– Direct paths to actions
– Customizability for agency
47
Jung, Yeonji , Promoting Actionability in Learning Analytics Through Design, Development, and Implementation
New York University ProQuest Dissertations Publishing, 2023. 30528476:
https://search.proquest.com/openview/8d9de164609f3f19e317bade08aa18dc/1?pq-origsite=gscholar&cbl=18750&diss=y

47. A Hybrid human-AI learning model
49
• Molenaar, I. (2021), "Personalisation of learning: Towards hybrid human-AI learning
technologies", in OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial
Intelligence, Blockchain and Robots, OECD Publishing, Paris,
https://doi.org/10.1787/2cc25e37-en.

48. Human-AI extended model (I)
Towards Human-AI collaboration
n Teacher monitors and controls
– the learning design prior to execution
(configuration phase)
– the orchestration of the lesson (runtime)
n Learner monitors and controls learning
– Orientation and planning prior to execution
– Monitoring and control during execution
– Reflection after execution
50

49. Human-AI extended model (II)
Timing and phases
Detect (data) 
Diagnose (technique/algorithm) 
Act (action)
Act components
– LA Perspective
n Inform, Advise, Guide, Recommend
– ITS Perspective
n Step, Task, Curriculum
51

50. Human-AI extended model (III)
n The transitions of control and monitoring have
profound implications for the professional
functioning (agency) of teachers
– Giving up task has positive sides (less time on
correction, more feedback)
– but also, negative sides (less insights and
control).
n This friction cannot be resolved easily but co-
creation processes do allow for a careful
articulation of this friction
52

51. Human-AI extended model (IV)
n Static or dynamic balance
– redesign and reconfiguration
– self-, co-, socially shared regulation
n Operators for teachers’ augmentation
– Transparency, agency, explainability, …
53

52. Hybrid Intelligence
54
D. Dellermann, P. Ebel, M. Soellner, J.M. Lerimesiter, “Hybrid Intelligence”, arXiv:2105.00691v1
[cs.AI]
“… the most likely paradigm for the division of labor between
humans and machines in the next years, or probably decades,
is hybrid intelligence. … to try to combine the complementary
strengths of heterogeneous intelligences (i.e., human and
artificial agents) into a socio-technological ensemble. We
envision hybrid intelligence systems, … to accomplish complex
goals by combining human and artificial intelligence to
collectively achieve superior results than each of the could have
done in separation and continuously improve by learning from
each other”

53. Hybrid Intelligence
55
Z. Akata et al., (2020) "A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect
With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence," Computer,
53(8), 18-28, doi: 10.1109/MC.2020.2996587
n “… Hybrid intelligence (HI) can go well beyond this by creating systems
that operate as mixed teams, where humans and machines cooperate
synergistically, proactively, and purposefully to achieve shared goals,
showing AI’s potential for amplifying instead of replacing human
intelligence”
n “Collaborative HI: How do we develop AI systems that work in
synergy with humans?
› Adaptive HI: How can these systems learn from and adapt to humans
and their environment?
› Responsible HI: How do we ensure that they behave ethically and
responsibly?
› Explainable HI: How can AI systems and humans share and explain
their awareness, goals, and strategies?”

54. And a few suggestions …
n Bring together LA and Learning Design (LD)
n Consider multiple needs and paths to use LA,
implemented as adaptive (by system/agent) or
adaptable (by users)
n Bring the teacher in the loop and orchestrate LA
with all stakeholders (OrLA)
n Consider the consolidated model for LA
n Adopt human-oriented workflows for LA solutions
n Consider data storytelling and explanatory LA
58

55. Illustrative study
65
From Theory to Action:
Developing and Evaluating Learning
Analytics for Learning Design
• K. Wiley Y. Dimitriadis, M. Linn (2023). “Human-Centered Learning Analytics approach for developing contextually scalable K-12
teacher dashboards”, British Journal of Educational Technology, https://doi.org/10.1111/bjet.13383
• Y. Dimitriadis, R. Martínez-Maldonado, K. Wiley (2021). Human-Centered Design Principles for Actionable Learning Analytics. In:
Tsiatsos T., Demetriadis S., Mikropoulos A., Dagdilelis V. (eds) Research on E-Learning and ICT in Education. Springer, Cham.
https://doi.org/10.1007/978-3-030-64363-8_15, 277-296
• K. Wiley, Y. Dimitriadis, A. Bradford, M. Linn (2020), “From Theory to Action: Developing and Evaluating Learning Analytics for
Learning Design”, Learning Analytics and Knowledge Conference (LAK 2020)

56. An overview of the study
n Design and development of Teacher Action Planner,
a LA tool that supports teachers’ orchestration
actions:
– Grounded on learning theory (Knowledge Integration)
and using the Inquiry Based Learning approach.
– Aligned with the Learning Design (Global Climate
Change and Photosynthesis Units) and platform (WISE)
– Aligned with stakeholders’ needs (OrLA)
– Functional within the constraints of the technical and
learning environments
66

57. DBR research approach
67
Sandoval & Bell (2004) – figure adapted from

58. The role of Theory: KI framework
68

59. LD informed by Learning Theory
69

60. The feature (and data) to focus on
70

61. Validating the usefulness of LA (I)
71
Are the data used to generate the learning analytics
useful for understanding student learning?

62. Validating the usefulness of LA (II)
72
High Number of Attempts Predicts Performance on
Subsequent Explanation Item

63. Optimizing the LD based on LA
73
Optimization: Fuse two steps of the unit

64. Creating a useful LA solution towards
pedagogical action
74

65. LA report
75

66. Evaluating the LA solution
76

67. The Teacher Action Planner (TAP)
77

68. Recommend orchestration actions
78

69. Human-AI collaboration
LA/AI to support doctoral student wellbeing
79
L.P. Prieto, P. Odriozola-González, M.J. Rodríguez-Triana, Y. Dimitriadis, T. Ley,
“Progress-Oriented Workshops for Doctoral Wellbeing: Evidence from a Two-Country Design-Based Research”,
International Journal of Doctoral Studies, 17, 39-66, 2022, https://doi.org/10.28945/4898
L.P. Prieto, G. Pishtari, Y. Dimitriadis, M.J. Rodríguez-Triana, T. Ley, P. Odriozola-Gonzélez.
“Single-case learning analytics: Feasibility of a human-centered analytics approach to support doctoral education”,

70. Human-AI collaboration
LA/AI to support doctoral student wellbeing
80
L.P. Prieto, P. Odriozola-González, M.J. Rodríguez-Triana, Y. Dimitriadis, T. Ley,
“Progress-Oriented Workshops for Doctoral Wellbeing: Evidence from a Two-Country Design-Based Research”,
International Journal of Doctoral Studies, 17, 39-66, 2022, https://doi.org/10.28945/4898
L.P. Prieto, G. Pishtari, Y. Dimitriadis, M.J. Rodríguez-Triana, T. Ley, P. Odriozola-Gonzélez.
“Single-case learning analytics: Feasibility of a human-centered analytics approach to support doctoral education”,

71. GenAI support to LD cycle
Human-AI Collaboration
81
D. Hernández-Leo, Y. Dimitriadis (2024): A human-centered perspective to Generative AI and Analytics Layers in Learning Design.
In M. Giannakos, R. Azevedo, P. Brusilovsky,M. Cukurova, Y. Dimitriadis, D. Hernandez-Leo, S.Jarvela, M. Mavrikis, B. Rienties,
(2024 March, Commntary paper) “The promise and challenges of Generative AI, such as LLMs, in education”,

72. Human-Centered Design of LA
n Eventually the benefits of enhanced agency,
adoption and impact of the LA/AI solutions
overcome the costs of difficult, time- and
resource-consuming participatory processes
n All the important aspects of learning
(cognitive, metacognitive, affective and social)
are highly sensible and dependent on the
context
82
Buckingham Shum, S., Ferguson, R., & Martinez-Maldonado, R. (2019). Human-Centred Learning
Analytics. Journal of Learning Analytics, 6(2), 1–9. https://doi.org/10.18608/jla.2019.62.1

73. Some take-home messages
n Learning Analytics (LA) and Artificial Intelligence in Education
(AIED), including Generative AI (GenAI), are powerful tools
and have a high potential in Education
n Fairness, Accountability, Transparency and Ethics (FATE) are
essential objectives to be achieved
n Human (Learner and Teacher) Agency is critical to take the
most out of LA/AIED
n Human-Centered approaches should be adopted for:
– Design of LA/AIED tools
– Learning Design and Orchestration by teachers
– Teacher and Student Augmentation
n Human-AI collaboration models and “scripts” are needed
84

74. The Future of HCLA?
S. Buckingham Shum, R. Martínez-Maldonado, Y. Dimitriadis, P. Santos (2024),
Human-Centred Learning Analytics: 2019–24, First published: 26 February 2024
https://doi.org/10.1111/bjet.13442

75. Let’s remember:
Learning Analytics (and AI-ED) are about
… Learning
… Learners
…Teachers
… Humans
… Society
This is why
Human-Centered Learning Analytics (and AI-ED)
may be worth considering