The \Citta Educante" project aims at radically rethinking the learning experience through advanced ICT technology to enrich and innovate didactic methods and tools. Among the project results, ExPLoRAA is an Intelligent Tutoring System, speci cally tailored for senior citizens, which, by integrating arti cial intelligence and state-of-the-art ICT techniques, is able to support older adults during visits to cultural locations in a city. In particular, ExPLoRAA integrates both the users' psycho-physiological aspects as well as geo-localization information and temporal constraints in the attempt to personalize the learning stimuli during the visit while favouring the concept of active ageing for the older people. After a generic introduction to the \Citta Educante" project, this paper presents both ExPLoRAA as a whole and some of the underlying solutions. Speci cally, the paper shows some of the choices that have been made to solve problems related to temporal exibility, supporting the dynamic adaptation of stimuli over time while ensuring the possibility for the users to further adapt a visit according to their current feelings. The paper describes both the choices made in the current system prototype and its embodiment in a concrete scenario which, by implementing a game similar to \treasure hunt", aims at fostering the physical and cognitive activity of the participating older people.
The \Citta Educante" project1 (the name means \city that educates" in
Italian) aims at proposing new educational approaches, enriching and innovating
methods and tools, overcoming the classical systems and the traditional role of
educators. An Intelligent Tutoring System (ITS) [
Common ITSs, typically, aim at replicating the bene ts of one-to-one
personalized tutoring in contexts where students would have access to one-to-many
instruction from a single teacher (e.g., classroom lectures), or no teacher at
all (e.g., on-line homework) [
Through the \Citta Educante" project, the theme of learning is framed in relation to the response to social challenges linked to the renewal of the educational system, to be achieved by means of the implementation of new learning/teaching models and/or the optimization of the existing ones on the various areas of life and knowledge, as well as new systems/evaluation processes, in which the technology (platforms and web) becomes an enabling factor.
Speci cally, the authors' goal in the project has been the one of thinking
an incarnation of the \Citta Educante" for the continuous education of older
people. In particular, considering the more recent experiences in the interaction
of elderlies with complex machines [
While pursuing the objective of the project, which aims at reformulating the
learning environments through the creation of platforms, services and ICT
applications, we got in touch with several volunteering organizations addressing,
speci cally, elderlies' needs. Among the di erent organizations, in particular,
Televita2 is a volunteering association whose main objective is to maintain the
elderlies active and motivated, leveraging upon individual aptitudes and/or
competencies [
Among the o ered services, we focused in particular on two speci c activities that were both in line with the \Citta Educante" concept and that are outlined in Figure 1: 2 http://www.televita.org { The AttivaMente laboratory : aims at keeping elders mentally active, so as to limit the cognitive decline associated with the advancement of age, by proposing them cognitive stimuli. Such stimuli, mostly consisting in general culture quiz and/or crosswords, are proposed to the elders in a context similar to a school lesson. Speci cally, by relying on some previous knowledge of the involved persons, as well as on their interactions during the lesson, a teacher, a volunteer himself, yet with more experience than others, controls the course's progress and slightly adapt it to the speci c context's needs. Since the stimuli are predetermined, however, such adaptations tend to be limited to the possibilities of the case. The use of arti cial intelligence techniques, in this case, could support the personalized delivery of stimuli by taking into account previous knowledge of the participants as well as their interactions with the AI system increasing, compared to the classical case, the personalization capabilities. { The Art History lessons and cultural events : analogously to the AttivaMente case, before attending to cultural events, some of the participants, according to their abilities and competencies, are asked to nd information about some speci c aspects of the event (e.g., a particular work of art within a museum, relevant historical happenings related to a visited site, etc.). Such information are then shared, in a \sort of" lesson with other participants in a classroom context and also outside during the event, enriching the overall knowledge of the group while encouraging the interaction among the members. Similarly to the AttivaMente case, such information may result to be limited and/or not customized to the speci c members of the event. AI techniques, in this case, might o er the opportunity to enrich the cultural events experience by providing further personalized stimuli to the event participants, as well as interaction requests to test the level of engagement and actively stimulate them.
Combining the above activities represents an opportunity for the elders to keep themselves active while learning in time and space. Additionally, the use of AI can support the development of more e ective and engaging learning experience.
The idea of developing the ExPLoRAA system is born as a consequence of a
eld experience. Speci cally, by taking inspiration from a previous work [
From a high-level point of view, the main modules of the system are described in Figure 2. In particular, it is possible to distinguish between two kinds of involved users: the students, i.e., a group of people, potentially, of any age, interested in using the learning services o ered by the ExPLoRAA environment, and the teachers, i.e., users with special privileges who have the opportunity to observe students, monitor the progress of the lessons and of the overall learning environment. The above users interact with the ExPLoRAA system which is composed of three functional blocks, intended as architectural subsystems, implementing the corresponding high-level functionalities: (i) the user modeling, whose goal is to create and maintain a user model and provide guidance for improving the learning process; (ii) the lesson modeling, whose role consists in combining the information from the previous subsystem and to create the customized lesson as well as to control its evolution; (iii) the lesson presentation, whose purpose is to represent the lesson through e ective graphical interfaces. Additionally, it is worth highlighting that the proposed system provides users, whether students or teachers, the opportunity to adapt the learning environment's evolution in real time by interpreting their decisions. In fact, the architecture is based on a sense-plan-act paradigm implementing, in a continuous loop, the three primitives (a) sense, in which information is collected from questions and sensors, (b) plan where a lesson blueprint is created, using the information available, and (c) act, in which the action, chosen by the planning process, is actually executed.
It is worth highlighting that, by exploiting mobile technology, the lesson
presentation module interacts remotely with the system allowing di erent learning
modalities: (a) on-site learning, closer to the classical teaching, in which the
technology is used as a support to the teaching in a classroom, with the aim
to create richer lessons, and (b) distributed learning, in which the technology
aims to support lessons outside the classroom during a practical experience.
More speci cally, in the on-site training modality, the system can be used by
a group of students at the same time. This mode represents an extension to
the classical learning method in which a teacher teaches to a group of students.
In this case, however, compared to the classical approach, the teaching is
enhanced by the introduction, within the lesson, of the ExPLoRAA technology.
Each lesson is instantiated by the teacher by de ning speci c learning objectives
[
Since most of the components of the ExPLoRAA system strongly depend on temporal aspects, we have chosen to rely on a speci c automated planning technique, called timeline-based, which allows to explicitly reason on time. Timelinebased planning, indeed, allows to reason about events in time and, hence, represents a valid tool for meeting our pedagogical needs. Planning a lesson, in particular, requires dispatching information at proper time. Additionally, reacting to users' interactions requires plan adaptation capabilities which can more hardly be achieved through other automatic planning techniques. Furthermore, the dynamic adaptation of the user pro les, which can take place on the di erent features that represent the user's model, can also be achieved through timelines.
As already said, timeline-based planning constitutes a technology that easily allows us to solve our problems. In order to better contextualize the choices that we have made and to better explain the di erent components of the system, however, it is worth introducing some basic formalism about constraint networks, on which timeline-based planning search strongly relies, and about the main concepts related to timeline-based planning. Speci cally, the main ingredients of constraint networks are variables and constraints.
De nition 1. A variable is an object that has a name and is able to take different values.
A variable (whose name is) x must be given a value from a set that is called the domain of x and is denoted by dom (x). The domain of a variable x may evolve in time but is always included in a set called initial domain. Depending on the nature of these domains, variables can be distinguished between continuous, having an in nite initial domain usually de ned in terms of real intervals, and discrete, whose initial domain contains a nite number of values. De nition 2. A constraint is a restriction on combinations of values that can be taken simultaneously by a set of variables.
A constraint c is de ned over a set of variables which constitute the scope of c and are denoted by scp (c). Finally, a structure composed of variables and constraints is called a constraint network.
De nition 3. A constraint network N is composed of a nite set of variables, denoted by vars (N ), and a nite set of constraints, denoted by cons (N ), such that 8c 2 cons (N ) ; scp (c) vars (N ).
Since constraint networks are fundamentals for timeline-based planning, it
is worth introducing some further concepts without going into too much formal
details. Speci cally, an assignment of values to some or all the variables is called
an evaluation. Furthermore, an evaluation is said to be consistent if it does not
violate any constraint. An evaluation is said to be complete if it includes all the
variables. Finally, given a constraint network, the problem of nding a consistent
and complete evaluation is called Constraint Satisfaction Problem (CSP) (refer
to [
As regards timeline-based planning, the main data structure is the timeline which, in generic terms, is a function of time over a nite domain. Values on the timelines are extracted from a set of temporally scoped predicates (i.e., predicates endowed with extra arguments belonging to the Time domain T, either real or discrete), with their parameters, called tokens. Formally, De nition 4. A token is an expression of the form: where n is a predicate name, x0; : : : ; xk are the predicate's parameters (i.e., constants, numeric variables or object variables), s and e are temporal parameters belonging to T such that s e and is a parameter (i.e., a constant or an object variable) representing the timeline on which the token apply.
The overall idea pursued in ExPLoRAA consists in using such tokens for representing the planned stimuli. Compared to the general formalization above, however, we can a ord some simpli cations. Speci cally, since the stimuli have no duration, the s and e variables of each token would always be equal. We address this by removing one of the two variables, e.g., the e variable. Additionally, since all the tokens will apply on a single \lesson" timeline, the variable would be a constant. For example, an expression st0 () @[10 : 00] would represent a stimulus st0 which is planned to happen at time 10:00.
It is worth noticing that the tokens' parameters, including the temporal ones,
are constituted, in general, by the variables of the constraint network as
introduced in the De nition 1. In order to reduce the allowed values for such
parameters, and thus decreasing the system's allowed behaviors, it is possible,
indeed, to impose constraints, as introduced in the De nition 2, among them
(and/or between the parameters and other possible variables). Such constraints
include temporal constraints, usually expressed by means of interval relations [
The set of tokens and constraints is used to describe the main data structure that will be used to represent the nodes of the timeline-based search space: the token network.
= (T ; C), where: { T = ft0; : : : ; tkg is a set of tokens. { C is a set of constraints, required to be consistent, on the variables of the tokens in T .
Additionally, tokens can be partitioned into two groups: facts and goals. While facts are, by de nition, inherently true, goals have to be achieved. Specifically, causality, in the timeline-based approach, is de ned by means of a set o rules indicating how to achieve goals. Formally, De nition 6. A rule is an expression of the form r where: { n (x0; : : : ; xk) @ [s; e; ] is the head of the rule, i.e., an expression in which n is a predicate name, x0; : : : ; xk are constants, numeric variables or object variables, s and e are temporal variables belonging to T such that s e and is an object variable representing the timeline on which the token apply. { r is the body of the rule (or the requirement), i.e., either a slave token, a constraint among tokens (possibly including the x0 : : : xk variables), a conjunction of requirements or a disjunction of requirements.
Rules de ne causal relations that must be complied to in order for a given goal to be achieved. For each goal having the form of the head of a rule, the body of the rule must also be present in the token network. As an example, the expression fst0 () @[s] fst1 () @[s1] ^ 10 s s1 20gg represents a rule asserting that, for each stimulus st0 there must exist, from 10 to 20 seconds before, a stimulus st1.
We have now all the ingredients to de ne a timeline-based planning problem. In particular, the de nition can rely on the above concept of requirement. De nition 7. A timeline-based planning problem is a triple P = (T ; R; r), where: { T is a set of timelines. { R is a set of rules. { r is a requirement, i.e., either a (fact or goal) token, a constraint among tokens, a conjunction of requirements or a disjunction of requirements.
The role of a timeline-based solver consists, basically, in applying the proper
rules to achieve all the goals of the problem while maintaining the constraint
network consistent. It is worth highlighting that, in general, the application
of the rules might result in the introduction of further goals into the token
network. Such goals, also called sub-goals, require to be achieved as well. The
process ends up when, for all the goals of the token network, either the body
of its corresponding rule is present in the token network or it is recognized as
semantically equivalent to another token (in this case we talk about uni cation
of the tokens). Notice that, despite the simplicity of the above solving procedure,
the combination of disjunctions and constraints in the rules make the resolution
process, in generally, extremely challenging from a computational point of view.
For this reason, indeed, heuristics are often used to make the resolution process
more e cient (see, for example, [
As already mentioned, the ExPLoRAA system is composed of di erent functional blocks. In particular, the user modeling module aims at creating and dynamically maintaining an updated model of the users which is used as a starting point for the personalization of the lessons. By pursuing the overall objective of enhancing the learning experience, indeed, it is necessary to keep a user model up-to-date in order to consider how their emotional, psychological, physiological and geographical parameters can in uence the learning process. Speci cally, the student modeling has three main objectives: { Model and monitor relevant factors through which the lesson can be customized; { Develop a model that can represent the user's pro le; { Provide a high level guidance for customizing learning objectives.
The set of considered relevant factors include, among the other things, the health status, the fatigue, the personal interests, the level of engagement and the current performance assessment. The use of Bluetooth bracelets (e.g., the Empatica E43), for example, allows the extraction of physiological values such as peripheral skin temperature, skin conductance, heart rate and heart variability. Additionally, it is possible to leverage on geo-localization services to get a good estimate of the users' position in time. The initial evaluation of these variables, used as a baseline to initialize the didactic experience and as a reference point for subsequent measurements, can be done through the use of standardized questionnaires or physiological measurements performed o -line before the lesson. It is worth to notice, however, that the pro le of a student can also be updated exploiting the interactions of the users with the system asking them, for example, to answer to sporadic questions. As an example, the users' engagement is measured through a ve levels Likert-type scale which is administered to the users at regular intervals. Finally, particular emphasis is given to the students' performance which is monitored and observed through the administration of questions and the interpretation of the provided answers.
By processing the above information, the system generates a user model that is constantly updated to perceive and represent signi cant changes in the emotional state (note that parameters can generally change over time). In addition, students' performance is analyzed and processed in order to gather further usable information useful for a ner customization of the lesson. Furthermore, the teacher can access this information in order to supervise and control the customization. For this purpose, this component can provide guidance on how to customize the lesson. Personalization of a training course can therefore be done automatically, but it can also be suggested to a teacher who independently decides whether to adapt the training course (i.e., according to a mixed-initiative style). 5.1
Modeling the Lessons The modeling of the lessons is the key feature of the ExPLoRAA system since it creates and manages the network of stimuli that guides the entire learning session. Nodes on this network are tokens and are intended to represent temporally annotated stimuli (e.g., videos, text messages, questions, etc.) to be sent, 3 https://www.empatica.com/research/e4 at appropriate time, to the users while edges represent causal and temporal relations among such stimuli introduced either in the planning problem de nition or through the application of the rules. Additionally, tokens are endowed with additional information including a set of covered topics (e.g., \art", \architecture", \religion", etc.) and some content dependent on the nature of the stimulus (e.g., a text for textual stimulus or a URL for a video stimulus). Speci cally, when a user enrolls to a lesson, de nes which of the topics covered by the lesson are of interest to her/him. When sending stimuli to the users, a ltering procedure will guarantee the delivery of only those stimuli for which the user has declared an interest.
It is worth noting that although the above network is initialized in order to represent an abstract blueprint of a lesson, it is afterwards customized and dynamically adapted to the pro le of the involved user. Personalization, indeed, takes place both in terms of users' interest in some topics, as explained earlier, as well as in terms of the type (and the number) of tokens in the token network. Speci cally, adaptations to the network are made thanks to the application of a set of rules (introduced in De nition 6) associated to each lesson, which de ne how to \react" to the users' pro le, to their updates and to their actions (e.g., moving to a speci c location or answering to a question). Such rules, in particular, are intended to create the \conditions", in terms of events and their relations within the network, for other events to be present. An example of rule can be \in order to stimulate the cognitive activity of the group, either propose a simple crosswords and the group's performance is low, or propose a complex crosswords and the group's performance is high". Notice that by taking advantage of the possibility of de ning disjunctions within the rules and being able to combine such rules sequentially, it is possible to obtain a great wealth of possible lessons' evolutions. Finally, since some of these rules may contain conditions which concern the user model, not all of them are applicable (e.g., in the above example, in case the group's current performance is low, only the simple crosswords is proposed), resulting in an overall network which is always compatible with the current users' pro les.
Broadly speaking, the teacher loads the chosen lesson from a database resulting in the construction of an initial token network corresponding to a set of stimuli, positioned over time, which will be communicated to users like videos, text messages, questions, etc. The network is, through the application of the rules, afterwards customized to the users participating in the lessons. By executing the lesson, then, tokens, representing stimuli and interaction requests, are dispatched, at proper time, to the users interested to them. It is worth noticing that in order to foster interaction and collaboration among them, the distributed information may be partial, requiring users the need to send messages to other students so that they can build an overview and respond appropriately to the challenges posed by the system, thus fostering cooperation. Whenever the prole of a user changes because, for example, her/his level of fatigue increases, speci c rules are applied resulting in an update of the network so as to bring it back to be \consistent" with the current status of the users. Similarly, updates might happen as a consequence of users' actions, resulting in a network which is always representative of the initial lesson while being dynamically adapted to the speci c context.
As an example, Figure 3 shows, at its top, an initial network containing three stimuli (i.e., st0, st1 and st3) and an interaction request (i.e., ir2) representing, for example, a question. Each event has its own execution time and its covered topics (e.g., st0 will be dispatched at 08:20). In the gure, the arrows represent the causal relations among the tokens that emerge from the application of the rules. In other words, the st1 token is in the network \because of" the st0 goal. Additionally, the st1 token is constrained to be from 15 to 40 seconds before the st0 goal. Notice that in order to simplify the explanation and make the speech clearer we have omitted the information regarding the other temporal constraints which, however, can be considered embedded within the arrows.
At 08:05, the interaction request ir2 requires the interested users (i.e., those whose interests match the topics of the stimulus associated to the ir2 token) to take some action (i.e., either a0 or a1). Suppose, as an example, that the action a0 is chosen, the network is adapted to the one on the left by adding the new token st4 at 08:30. Again, the st4 token is added within the network \because of" the a0 action. Conversely, in case action a1 is chosen, the network is adapted to the one on the right by adding the new tokens st5, at time 08:35, and st6 at 08:38. As already mentioned, it is worth noticing that this type of adaptation occurs in a similar way as a consequence of the actions carried out by the users as well as for the dynamic changes that occur to their pro le, ensuring a discrete availability of exibility and adaptation to the particular conditions that may arise in the di erent lessons.
An interesting feature of the proposed system regards the possibility for
adapting the lessons thanks to the supported temporal exibility. In particular, the
constraint network introduced in De nition 3 manages temporal variables and
constraints through a Simple Temporal Network (STN) (see [
While in a standard lesson execution we would use these values to establish the timing for dispatching the stimuli, problems arise when the teachers want to control their position in time. Suppose, for example, we have an STN with two variables x0 and x1, representing the time points of two tokens associated to two stimuli st0 and st1. Additionally, the 10 x1 x0 20 constraint imposes a temporal distance between them which goes from 10 to 20 time units (although the system uses milliseconds, we maintain in the dissertation a generic measurement unit for sake of simplicity). The initial bounds of the x0 variable is [10; 30] while the initial bounds of the x1 variable is [20; 50]. Since the earliest possible times represent a solution for such a constraint network, st0 and st1 are going to be dispatched, respectively, at time 10 and at time 20. Suppose, for any didactic reason, the teacher wants to delay the execution time of the stimulus st0 of 15 time units, a temporal constraint would reduce the bounds of the x0 variable to [25; 30]. The propagation of the constraints would tighten the bound of the x1 variable as well to, possibly (notice that other constraints might also be involved), [35; 50], hence right-shifting the dispatching time of st1 at 35. At a second step, however, the teacher recognizes she/he has moved the stimulus st1 too far and decides it is better to move it a little back. The lower bound for the st1 stimulus, however, is now 35 and cannot be moved back through temporal constraints without introducing an inconsistency in the constraint network.
We can get around this problem by introducing, for each variable x, together with its lower (i.e., lx) and upper (i.e., ux) bounds resulting from the propagation of the temporal constraints, a third value dx representing its dispatching time, whose allowed values are always maintained between the bounds of the variable (i.e., the relation lx dx ux always holds). Whenever the user decides to 4 It is worth noticing that the same result holds also when choosing for each variable their latest possible times. change the position in time of a stimulus, however, only the dispatching times are updated, without touching the bounds of any variable.
In order to implement such a procedure we need, rst of all, for each temporal variable x, a list of watching constraints watchs [x] (i.e., for each variable, the list of constraint watching for the variable's updates). Speci cally, when a new constraint c is introduced into the constraint network, c is added to the watching lists watchs [xi] of each variable xi 2 scp (c) belonging to the scope of c. Additionally, we need a propagation queue propq responsible for maintaining the list of variables whose dispatching time has been updated. Whenever the dispatching time of a variable is updated, the variable is enqueued into the propagation queue.
procedure propagate while propq 6= ; do x propq:dequeue for all c 2 watches [x] do c.propagate(x)
The propagation procedure is described in the Algorithm 1. Speci cally, while the propagation queue is not empty, a variable x is dequeued from the queue and a propagation procedure is called for each constraint c in the watching list associated to the variable x (i.e., for all the constraints having the variable x in their scope). Additionally, for each temporal constraint c having the form l xj xi u the propagation procedure is described in the Algorithm 2. Speci cally, whenever the dispatching time of the xi (xj) variable is updated, the propagation procedure checks whether the xj (xi) variable's dispatching time needs to be updated as well. In case it needs, the xj (xi) variable's dispatching time is updated and the xj (xi) variable is enqueued in the propq propagation queue.
The last aspect to be considered regards the initial introduction of the variables within the propagation queue. There are basically two reasons which might lead to this: either (a) a bound of a variable x is updated, within the STN propagation procedure, in a way such that the expression lx dx ux is violated or (b) a user manually updates the dispatching time of a variable x associated to a stimulus. In the rst case, described in the Algorithm 3, the dispatching time is updated so as to restore the validity of the lx dx ux relation and the variable is enqueued in propq. The second case, described in the Algorithm 4, more straightforwardly, directly updates the dispatching time and enqueues the varible in propq. Notice that, after calling both the STN propagation procedure and the set dspatching time procedure, the propagate procedure (Algorithm 1) must be called in order to update the dispatching times of the related variables.
Finally, it is worth noticing that none of the above procedures is supposed to fail. The consistency checking of the constraint network, indeed, is still delegated to the STN propagation procedure.
Algorithm 2 The propagation procedure for the temporal constraint Algorithm 3 The bound update procedure called by the STN propagation procedure procedure set bound(x; l; u) lx l; ux u if dx < lx then dx lx propq propq [ fxg else if dx > ux then
dx dx propq
propq [ fxg Algorithm 4 The dispatching time update procedure procedure set dispatching time(x; d) if dx 6= d then dx d propq
propq [ fxg
By relying on the concept sketched in Figure 2, we have realized a rst prototype of the ExPLoRAA ITS. In particular, several instances of the Desktop and Mobile applications, depicted in Figure 4, allow students and teachers the access to the ExPLoRAA system through the remote interaction with a centralized back-end. Speci cally, teachers have the opportunity to create and manage lessons while monitoring the students following them. Students, on the other hand, enroll to the available lessons specifying their interests and receive customized stimuli according to their psychophysiological state assessed through a combination of sensors and targeted questions.
Fig. 4. The ExPLoRAA Desktop and Mobile Graphical User Interfaces.
We have created a lesson model example inspired by the \Madonnelle Stradaiole" of Rome. These are present in large numbers on the walls or corners of historic buildings. The idea consists in sending stimuli to users so as to guide them to visit such shrines, customizing the path to their psychophysiological state. By exploiting georeferencing, the ExPLoRAA system asks users to take pictures of the shrines to be used, afterwards, to build games which, played together, reduce social isolation and stimulate cognitive activity. The rst step for the implementation of a scenario of this kind consists in collecting information about the shrines like, for example, a brief history about each of them and their GPS coordinates. Additionally, a customized questionnaire, administered to users, is intended to extract an initial pro le to be used as a baseline. Starting by the initial pro le, a path, compatible with the pro le, is selected and, step by step and at proper time, suggested to the user (e.g., \go to pos1", \the shrine at pos1 has been built in 1796", \take a picture at the shrine in pos1", \go to pos4", etc.). By taking into account physiological data, the system can adapt the route switching, for example, once the ExPLoRAA system realizes that the user is not too tired, to a longer one, fostering physical activity and a prolonged interaction with the other involved users. Finally, once back from the trip, the ExPLoRAA system builds a \memory" game, challenging the participants to discover, in the fewest possible steps, pairs of the taken pictures hidden under the tiles, hence stimulating cognitive activity.
Feedback gathering has taken place at the Televita facilities. Speci cally, a focus group has been held in which volunteers of the association, as well as representatives of other associations that deal with assistance to the elderly and/or psychological distress present on the territory of the III Municipality of Rome, have participated. Speci cally, 6 people dealing with Televita, 3 representatives of \Societa San Vincenzo de Paoli", and 1 representative of the volunteering organization \Oltre Le Barriere". The meeting (see Figure 5) has been structured around the presentation of the developed prototype that proved to be functional for eliciting comments and suggestions from the participants. We wanted to address the discussion in the direction of feedback collection for what concerns the improvement of the interface, the modalities of providing the service, the ways to enhance the engagement, as well as the possibility for the participants to provide suggestions and free comments.
In general, the feedback obtained on the platform and its services was decidedly positive and the participants found the play aspect of the project particularly interesting. In fact, the game was read not only as a recreative tool, but also as a learning and social cohesion one, a crucial aspect to promote well-being in the elderly. Furthermore, the value of the possibility o ered to citizens who can participate in the co-management of the public good through the municipal volunteering has emerged and, thanks to ExPLoRAA, they have the opportunity to take on some aspects in a territorial area thus favoring a certain sense of belonging. This re ects the need to motivate older people to feel active, rather than a burden for society or for the family. Participation in activities motivates people and makes them feel alive. Determinant is also trying to cope with one of the biggest problems of older people: the su ering of loneliness. It emerged, indeed, that the proposed formula could represent an e ective tool, especially considering the social character of the activities that are essentially based on the aggregation and comparison between di erent people in a game context. The possibility of personalizing the experience was judged to be very useful, both to encourage the motivation and involvement of people during the activity as well as to provide access to a wider range of users, taking into account possible physical limitations. In this sense, the personalization of services is not de ned only by psicological aspects, such as the level of engagement, attention, emotional state, but also by more purely physical factors, such as fatigue. According to the participants, these characteristics make the proposal even more e ective and desirable. Finally, a very interesting aspect that emerged from the interaction with the volunteers concerns the possibility of using the ExPLoRAA system to favor inter-generational exchange. In fact, much emphasis has been given to the possibility of exploiting the system to make elderly and young people interact and to promote the comparison. In fact, a system focused on aspects such as sharing and group activities favors the exchange, while taking into account individual preferences and needs, thanks to the possibility of personalizing the experience. 8
By pursuing their didactic needs, teachers want exibility in adapting the resulting network. Furthermore, teachers are human beings and, as such, might change their idea. This paper introduces the ExPLoRAA system as an AIbased learning environment specialized so as to support active aging. We have introduced the general idea of supporting older people in maintaining themselves mentally and physically active being helped by some intelligent technology both during a class and during excursions. The ICT intelligent core makes use of a speci c kind of arti cial intelligence technology called automated planning which is responsible for representing the key ingredients of the ExPLoRAA system, creating a baseline \lesson" and dynamically adapting it to actions and pro le updates so as to integrate both personalized stimuli and requests to the users over time. Additionally, the proposed system guarantees an e cient management of the temporal constraints and, thanks to the introduction of the proposed algorithm, the possibility for the teachers to e ciently change their minds regarding decisions made on temporal aspects. Although the application is currently under development, the constant contact with Televita's volunteers is allowing the transition from a lab prototype to an incrementally more robust version of the system to be tested in realistic scenarios.